Overview

Dataset statistics

Number of variables198
Number of observations1460
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory861.3 KiB
Average record size in memory604.1 B

Variable types

Numeric25
Categorical173

Warnings

Utilities_was_missing has constant value "0" Constant
MSZoning_was_missing has constant value "0" Constant
GarageCars_was_missing has constant value "0" Constant
GarageArea_was_missing has constant value "0" Constant
SaleType_was_missing has constant value "0" Constant
LotShape_was_missing has constant value "0" Constant
GarageQual is highly correlated with GarageCond and 5 other fieldsHigh correlation
GarageCond is highly correlated with GarageQual and 5 other fieldsHigh correlation
GarageType_was_missing is highly correlated with GarageQual and 5 other fieldsHigh correlation
GarageFinish_was_missing is highly correlated with GarageQual and 5 other fieldsHigh correlation
GarageQual_was_missing is highly correlated with GarageQual and 5 other fieldsHigh correlation
GarageCond_was_missing is highly correlated with GarageQual and 5 other fieldsHigh correlation
RoofStyle_Gable is highly correlated with RoofStyle_HipHigh correlation
RoofStyle_Hip is highly correlated with RoofStyle_GableHigh correlation
Exterior1st_CBlock is highly correlated with Exterior2nd_CBlockHigh correlation
Exterior1st_CemntBd is highly correlated with Exterior2nd_CmentBdHigh correlation
Exterior1st_MetalSd is highly correlated with Exterior2nd_MetalSdHigh correlation
Exterior1st_VinylSd is highly correlated with Exterior2nd_VinylSdHigh correlation
Exterior2nd_CBlock is highly correlated with Exterior1st_CBlockHigh correlation
Exterior2nd_CmentBd is highly correlated with Exterior1st_CemntBdHigh correlation
Exterior2nd_MetalSd is highly correlated with Exterior1st_MetalSdHigh correlation
Exterior2nd_VinylSd is highly correlated with Exterior1st_VinylSdHigh correlation
GarageType_NA is highly correlated with GarageQual and 5 other fieldsHigh correlation
SaleType_New is highly correlated with SaleCondition_PartialHigh correlation
SaleCondition_Partial is highly correlated with SaleType_NewHigh correlation
Exterior2nd_Stone is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Condition2_RRAn is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Exterior1st_Plywood is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
CentralAir is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
MSZoning_FV is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
HouseStyle_1Story is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
BldgType_Duplex is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Neighborhood_NridgHt is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
SaleType_ConLw is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Neighborhood_Blueste is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Neighborhood_StoneBr is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Electrical_Mix is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Exterior1st_CBlock is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Exterior1st_Stone is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Neighborhood_NAmes is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
SaleType_ConLD is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
BsmtCond is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Utilities_was_missing is highly correlated with Exterior2nd_Stone and 171 other fieldsHigh correlation
Neighborhood_SawyerW is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
SaleType_Con is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Exterior1st_ImStucc is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
LandContour_Low is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Neighborhood_BrkSide is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Condition1_RRNn is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
MasVnrType_BrkFace is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Neighborhood_BrDale is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Exterior2nd_Other is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
LotConfig_FR2 is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Neighborhood_Edwards is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
LandContour_Lvl is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Exterior1st_CemntBd is highly correlated with Utilities_was_missing and 6 other fieldsHigh correlation
RoofStyle_Shed is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Foundation_PConc is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
MSZoning_RM is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
MasVnrType_Stone is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
SaleCondition_Family is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Neighborhood_Mitchel is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
HalfBath is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
GarageArea_was_missing is highly correlated with Exterior2nd_Stone and 171 other fieldsHigh correlation
Exterior2nd_VinylSd is highly correlated with Utilities_was_missing and 6 other fieldsHigh correlation
GarageFinish is highly correlated with Utilities_was_missing and 10 other fieldsHigh correlation
Heating_GasA is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
GarageCond_was_missing is highly correlated with Utilities_was_missing and 11 other fieldsHigh correlation
Exterior2nd_Wd Sdng is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
GarageType_Detchd is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
SaleType_Oth is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
FireplaceQu is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Exterior1st_Stucco is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
GarageType_Basment is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
SaleCondition_AdjLand is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Condition2_Norm is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
LotConfig_CulDSac is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Neighborhood_Sawyer is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Neighborhood_CollgCr is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Condition1_RRNe is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
BsmtFullBath is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
RoofStyle_Gambrel is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
MSZoning_RL is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
HouseStyle_2Story is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
LandContour_HLS is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
GarageType_BuiltIn is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Exterior2nd_Stucco is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
RoofMatl_WdShngl is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Exterior2nd_HdBoard is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Condition2_RRAe is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Neighborhood_IDOTRR is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Foundation_Wood is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Exterior1st_BrkFace is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Neighborhood_Veenker is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
YrSold is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
LotConfig_Inside is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Heating_Wall is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Condition1_Norm is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Condition2_Feedr is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
GarageFinish_was_missing is highly correlated with Utilities_was_missing and 11 other fieldsHigh correlation
Neighborhood_NPkVill is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Neighborhood_ClearCr is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
LotShape_was_missing is highly correlated with Exterior2nd_Stone and 171 other fieldsHigh correlation
Exterior1st_HdBoard is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Utilities is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Condition1_RRAe is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Condition2_RRNn is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
RoofMatl_Membran is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
KitchenQual is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Neighborhood_SWISU is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
SaleType_New is highly correlated with Utilities_was_missing and 6 other fieldsHigh correlation
Neighborhood_NoRidge is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
SaleType_was_missing is highly correlated with Exterior2nd_Stone and 171 other fieldsHigh correlation
BsmtExposure is highly correlated with Utilities_was_missing and 6 other fieldsHigh correlation
ExterQual is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Fireplaces is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
SaleType_ConLI is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
SaleType_CWD is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Condition1_PosA is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
RoofMatl_Roll is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
GarageCars is highly correlated with Utilities_was_missing and 10 other fieldsHigh correlation
HouseStyle_2.5Fin is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Exterior2nd_Brk Cmn is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Condition1_PosN is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Exterior2nd_CBlock is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
HouseStyle_2.5Unf is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
SaleCondition_Normal is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
RoofStyle_Gable is highly correlated with Utilities_was_missing and 6 other fieldsHigh correlation
BldgType_Twnhs is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
RoofMatl_Metal is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
RoofMatl_WdShake is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
BsmtHalfBath is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
GarageType_was_missing is highly correlated with Utilities_was_missing and 11 other fieldsHigh correlation
Neighborhood_Somerst is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Exterior1st_VinylSd is highly correlated with Utilities_was_missing and 6 other fieldsHigh correlation
Electrical_FuseF is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Exterior2nd_CmentBd is highly correlated with Utilities_was_missing and 6 other fieldsHigh correlation
RoofStyle_Mansard is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
GarageType_NA is highly correlated with Utilities_was_missing and 11 other fieldsHigh correlation
GarageType_Attchd is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
ExterCond is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Electrical_SBrkr is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Condition1_RRAn is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Exterior1st_MetalSd is highly correlated with Utilities_was_missing and 6 other fieldsHigh correlation
BldgType_2fmCon is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
KitchenAbvGr is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Street is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Neighborhood_Timber is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
MSZoning_was_missing is highly correlated with Exterior2nd_Stone and 171 other fieldsHigh correlation
Exterior2nd_Wd Shng is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
HouseStyle_1.5Unf is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
HouseStyle_SFoyer is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
FullBath is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
RoofStyle_Hip is highly correlated with Utilities_was_missing and 6 other fieldsHigh correlation
SaleType_WD is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
HeatingQC is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Foundation_Slab is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Exterior2nd_ImStucc is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Exterior2nd_MetalSd is highly correlated with Utilities_was_missing and 6 other fieldsHigh correlation
LandSlope_Mod is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
RoofMatl_Tar&Grv is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
BsmtExposure_was_missing is highly correlated with Utilities_was_missing and 6 other fieldsHigh correlation
LotConfig_FR3 is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
MasVnrType_None is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
BldgType_TwnhsE is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
HouseStyle_SLvl is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Condition2_PosN is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Neighborhood_MeadowV is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
LandSlope_Sev is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
GarageCars_was_missing is highly correlated with Exterior2nd_Stone and 171 other fieldsHigh correlation
Condition2_PosA is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Heating_Grav is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
MSZoning_RH is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Exterior1st_BrkComm is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
LotShape is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Neighborhood_NWAmes is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
RoofMatl_CompShg is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Electrical_FuseP is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Foundation_Stone is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
GarageType_CarPort is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Neighborhood_OldTown is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Exterior1st_Wd Sdng is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
SaleCondition_Alloca is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Foundation_CBlock is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
BsmtQual is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Exterior2nd_AsphShn is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Condition1_Feedr is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Exterior1st_AsphShn is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Exterior1st_WdShing is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Neighborhood_Crawfor is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Exterior2nd_Plywood is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
GarageQual_was_missing is highly correlated with Utilities_was_missing and 11 other fieldsHigh correlation
Heating_OthW is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
SaleCondition_Partial is highly correlated with Utilities_was_missing and 6 other fieldsHigh correlation
Neighborhood_Gilbert is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
PavedDrive is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Exterior2nd_BrkFace is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Heating_GasW is highly correlated with Utilities_was_missing and 5 other fieldsHigh correlation
Id is uniformly distributed Uniform
Id has unique values Unique
BsmtFinSF1 has 467 (32.0%) zeros Zeros
BsmtUnfSF has 118 (8.1%) zeros Zeros
TotalBsmtSF has 37 (2.5%) zeros Zeros
GarageArea has 81 (5.5%) zeros Zeros
GarageQual has 81 (5.5%) zeros Zeros
GarageCond has 81 (5.5%) zeros Zeros
OpenPorchSF has 656 (44.9%) zeros Zeros

Reproduction

Analysis started2021-05-01 14:43:09.549015
Analysis finished2021-05-01 14:46:39.874229
Duration3 minutes and 30.33 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

Id
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct1460
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean730.5
Minimum1
Maximum1460
Zeros0
Zeros (%)0.0%
Memory size11.5 KiB
2021-05-01T17:46:39.950836image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile73.95
Q1365.75
median730.5
Q31095.25
95-th percentile1387.05
Maximum1460
Range1459
Interquartile range (IQR)729.5

Descriptive statistics

Standard deviation421.6100094
Coefficient of variation (CV)0.577152648
Kurtosis-1.2
Mean730.5
Median Absolute Deviation (MAD)365
Skewness0
Sum1066530
Variance177755
MonotocityStrictly increasing
2021-05-01T17:46:40.063604image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
0.1%
9821
 
0.1%
9801
 
0.1%
9791
 
0.1%
9781
 
0.1%
9771
 
0.1%
9761
 
0.1%
9751
 
0.1%
9741
 
0.1%
9731
 
0.1%
Other values (1450)1450
99.3%
ValueCountFrequency (%)
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
91
0.1%
101
0.1%
ValueCountFrequency (%)
14601
0.1%
14591
0.1%
14581
0.1%
14571
0.1%
14561
0.1%
14551
0.1%
14541
0.1%
14531
0.1%
14521
0.1%
14511
0.1%

MSSubClass
Real number (ℝ≥0)

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.89726027
Minimum20
Maximum190
Zeros0
Zeros (%)0.0%
Memory size11.5 KiB
2021-05-01T17:46:40.158097image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q120
median50
Q370
95-th percentile160
Maximum190
Range170
Interquartile range (IQR)50

Descriptive statistics

Standard deviation42.30057099
Coefficient of variation (CV)0.7434553226
Kurtosis1.580187965
Mean56.89726027
Median Absolute Deviation (MAD)30
Skewness1.407656747
Sum83070
Variance1789.338306
MonotocityNot monotonic
2021-05-01T17:46:40.232175image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
20536
36.7%
60299
20.5%
50144
 
9.9%
12087
 
6.0%
3069
 
4.7%
16063
 
4.3%
7060
 
4.1%
8058
 
4.0%
9052
 
3.6%
19030
 
2.1%
Other values (5)62
 
4.2%
ValueCountFrequency (%)
20536
36.7%
3069
 
4.7%
404
 
0.3%
4512
 
0.8%
50144
 
9.9%
60299
20.5%
7060
 
4.1%
7516
 
1.1%
8058
 
4.0%
8520
 
1.4%
ValueCountFrequency (%)
19030
 
2.1%
18010
 
0.7%
16063
 
4.3%
12087
 
6.0%
9052
 
3.6%
8520
 
1.4%
8058
 
4.0%
7516
 
1.1%
7060
 
4.1%
60299
20.5%

LotFrontage
Real number (ℝ≥0)

Distinct110
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.26712329
Minimum21
Maximum313
Zeros0
Zeros (%)0.0%
Memory size11.5 KiB
2021-05-01T17:46:40.834079image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile35.95
Q160
median63
Q379
95-th percentile104
Maximum313
Range292
Interquartile range (IQR)19

Descriptive statistics

Standard deviation22.35635469
Coefficient of variation (CV)0.3274834739
Kurtosis21.17281751
Mean68.26712329
Median Absolute Deviation (MAD)10
Skewness2.504091646
Sum99670
Variance499.8065949
MonotocityNot monotonic
2021-05-01T17:46:40.935566image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60402
27.5%
7070
 
4.8%
8069
 
4.7%
5057
 
3.9%
7553
 
3.6%
6544
 
3.0%
8540
 
2.7%
7825
 
1.7%
9023
 
1.6%
2123
 
1.6%
Other values (100)654
44.8%
ValueCountFrequency (%)
2123
1.6%
2419
1.3%
306
 
0.4%
325
 
0.3%
331
 
0.1%
3410
0.7%
359
 
0.6%
366
 
0.4%
375
 
0.3%
381
 
0.1%
ValueCountFrequency (%)
3132
0.1%
1821
0.1%
1742
0.1%
1681
0.1%
1601
0.1%
1531
0.1%
1521
0.1%
1501
0.1%
1491
0.1%
1441
0.1%

LotArea
Real number (ℝ≥0)

Distinct1073
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10516.82808
Minimum1300
Maximum215245
Zeros0
Zeros (%)0.0%
Memory size11.5 KiB
2021-05-01T17:46:41.049155image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1300
5-th percentile3311.7
Q17553.5
median9478.5
Q311601.5
95-th percentile17401.15
Maximum215245
Range213945
Interquartile range (IQR)4048

Descriptive statistics

Standard deviation9981.264932
Coefficient of variation (CV)0.949075601
Kurtosis203.243271
Mean10516.82808
Median Absolute Deviation (MAD)1998
Skewness12.20768785
Sum15354569
Variance99625649.65
MonotocityNot monotonic
2021-05-01T17:46:41.160244image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
720025
 
1.7%
960024
 
1.6%
600017
 
1.2%
840014
 
1.0%
900014
 
1.0%
1080014
 
1.0%
168010
 
0.7%
75009
 
0.6%
81258
 
0.5%
61208
 
0.5%
Other values (1063)1317
90.2%
ValueCountFrequency (%)
13001
 
0.1%
14771
 
0.1%
14911
 
0.1%
15261
 
0.1%
15332
 
0.1%
15961
 
0.1%
168010
0.7%
18691
 
0.1%
18902
 
0.1%
19201
 
0.1%
ValueCountFrequency (%)
2152451
0.1%
1646601
0.1%
1590001
0.1%
1151491
0.1%
707611
0.1%
638871
0.1%
572001
0.1%
535041
0.1%
532271
0.1%
531071
0.1%

Street
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size84.2 KiB
20
1454 
10
 
6

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2920
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20
2nd row20
3rd row20
4th row20
5th row20
ValueCountFrequency (%)
201454
99.6%
106
 
0.4%
2021-05-01T17:46:41.338064image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:41.391017image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
201454
99.6%
106
 
0.4%

Most occurring characters

ValueCountFrequency (%)
01460
50.0%
21454
49.8%
16
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2920
100.0%

Most frequent character per category

ValueCountFrequency (%)
01460
50.0%
21454
49.8%
16
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common2920
100.0%

Most frequent character per script

ValueCountFrequency (%)
01460
50.0%
21454
49.8%
16
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII2920
100.0%

Most frequent character per block

ValueCountFrequency (%)
01460
50.0%
21454
49.8%
16
 
0.2%

LotShape
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size84.2 KiB
40
925 
30
484 
20
 
41
10
 
10

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2920
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row40
2nd row40
3rd row30
4th row30
5th row30
ValueCountFrequency (%)
40925
63.4%
30484
33.2%
2041
 
2.8%
1010
 
0.7%
2021-05-01T17:46:41.526958image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:41.582630image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
40925
63.4%
30484
33.2%
2041
 
2.8%
1010
 
0.7%

Most occurring characters

ValueCountFrequency (%)
01460
50.0%
4925
31.7%
3484
 
16.6%
241
 
1.4%
110
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2920
100.0%

Most frequent character per category

ValueCountFrequency (%)
01460
50.0%
4925
31.7%
3484
 
16.6%
241
 
1.4%
110
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common2920
100.0%

Most frequent character per script

ValueCountFrequency (%)
01460
50.0%
4925
31.7%
3484
 
16.6%
241
 
1.4%
110
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2920
100.0%

Most frequent character per block

ValueCountFrequency (%)
01460
50.0%
4925
31.7%
3484
 
16.6%
241
 
1.4%
110
 
0.3%

Utilities
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size84.2 KiB
40
1459 
20
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2920
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row40
2nd row40
3rd row40
4th row40
5th row40
ValueCountFrequency (%)
401459
99.9%
201
 
0.1%
2021-05-01T17:46:41.729916image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:41.781233image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
401459
99.9%
201
 
0.1%

Most occurring characters

ValueCountFrequency (%)
01460
50.0%
41459
50.0%
21
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2920
100.0%

Most frequent character per category

ValueCountFrequency (%)
01460
50.0%
41459
50.0%
21
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common2920
100.0%

Most frequent character per script

ValueCountFrequency (%)
01460
50.0%
41459
50.0%
21
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2920
100.0%

Most frequent character per block

ValueCountFrequency (%)
01460
50.0%
41459
50.0%
21
 
< 0.1%

OverallQual
Real number (ℝ≥0)

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.099315068
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Memory size11.5 KiB
2021-05-01T17:46:41.832042image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median6
Q37
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.382996547
Coefficient of variation (CV)0.2267462053
Kurtosis0.09629277836
Mean6.099315068
Median Absolute Deviation (MAD)1
Skewness0.2169439278
Sum8905
Variance1.912679448
MonotocityNot monotonic
2021-05-01T17:46:41.906938image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5397
27.2%
6374
25.6%
7319
21.8%
8168
11.5%
4116
 
7.9%
943
 
2.9%
320
 
1.4%
1018
 
1.2%
23
 
0.2%
12
 
0.1%
ValueCountFrequency (%)
12
 
0.1%
23
 
0.2%
320
 
1.4%
4116
 
7.9%
5397
27.2%
6374
25.6%
7319
21.8%
8168
11.5%
943
 
2.9%
1018
 
1.2%
ValueCountFrequency (%)
1018
 
1.2%
943
 
2.9%
8168
11.5%
7319
21.8%
6374
25.6%
5397
27.2%
4116
 
7.9%
320
 
1.4%
23
 
0.2%
12
 
0.1%

OverallCond
Real number (ℝ≥0)

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.575342466
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Memory size11.5 KiB
2021-05-01T17:46:41.980768image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median5
Q36
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.112799337
Coefficient of variation (CV)0.1995930014
Kurtosis1.106413461
Mean5.575342466
Median Absolute Deviation (MAD)0
Skewness0.6930674725
Sum8140
Variance1.238322364
MonotocityNot monotonic
2021-05-01T17:46:42.059242image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
5821
56.2%
6252
 
17.3%
7205
 
14.0%
872
 
4.9%
457
 
3.9%
325
 
1.7%
922
 
1.5%
25
 
0.3%
11
 
0.1%
ValueCountFrequency (%)
11
 
0.1%
25
 
0.3%
325
 
1.7%
457
 
3.9%
5821
56.2%
6252
 
17.3%
7205
 
14.0%
872
 
4.9%
922
 
1.5%
ValueCountFrequency (%)
922
 
1.5%
872
 
4.9%
7205
 
14.0%
6252
 
17.3%
5821
56.2%
457
 
3.9%
325
 
1.7%
25
 
0.3%
11
 
0.1%

YearBuilt
Real number (ℝ≥0)

Distinct112
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1971.267808
Minimum1872
Maximum2010
Zeros0
Zeros (%)0.0%
Memory size11.5 KiB
2021-05-01T17:46:42.160945image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1872
5-th percentile1916
Q11954
median1973
Q32000
95-th percentile2007
Maximum2010
Range138
Interquartile range (IQR)46

Descriptive statistics

Standard deviation30.20290404
Coefficient of variation (CV)0.01532156307
Kurtosis-0.4395519416
Mean1971.267808
Median Absolute Deviation (MAD)25
Skewness-0.6134611725
Sum2878051
Variance912.2154126
MonotocityNot monotonic
2021-05-01T17:46:42.271455image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200667
 
4.6%
200564
 
4.4%
200454
 
3.7%
200749
 
3.4%
200345
 
3.1%
197633
 
2.3%
197732
 
2.2%
192030
 
2.1%
195926
 
1.8%
199825
 
1.7%
Other values (102)1035
70.9%
ValueCountFrequency (%)
18721
 
0.1%
18751
 
0.1%
18804
 
0.3%
18821
 
0.1%
18852
 
0.1%
18902
 
0.1%
18922
 
0.1%
18931
 
0.1%
18981
 
0.1%
190010
0.7%
ValueCountFrequency (%)
20101
 
0.1%
200918
 
1.2%
200823
 
1.6%
200749
3.4%
200667
4.6%
200564
4.4%
200454
3.7%
200345
3.1%
200223
 
1.6%
200120
 
1.4%

YearRemodAdd
Real number (ℝ≥0)

Distinct61
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1984.865753
Minimum1950
Maximum2010
Zeros0
Zeros (%)0.0%
Memory size11.5 KiB
2021-05-01T17:46:42.384068image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1950
5-th percentile1950
Q11967
median1994
Q32004
95-th percentile2007
Maximum2010
Range60
Interquartile range (IQR)37

Descriptive statistics

Standard deviation20.64540681
Coefficient of variation (CV)0.01040141217
Kurtosis-1.272245192
Mean1984.865753
Median Absolute Deviation (MAD)13
Skewness-0.5035620027
Sum2897904
Variance426.2328223
MonotocityNot monotonic
2021-05-01T17:46:42.494082image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1950178
 
12.2%
200697
 
6.6%
200776
 
5.2%
200573
 
5.0%
200462
 
4.2%
200055
 
3.8%
200351
 
3.5%
200248
 
3.3%
200840
 
2.7%
199636
 
2.5%
Other values (51)744
51.0%
ValueCountFrequency (%)
1950178
12.2%
19514
 
0.3%
19525
 
0.3%
195310
 
0.7%
195414
 
1.0%
19559
 
0.6%
195610
 
0.7%
19579
 
0.6%
195815
 
1.0%
195918
 
1.2%
ValueCountFrequency (%)
20106
 
0.4%
200923
 
1.6%
200840
2.7%
200776
5.2%
200697
6.6%
200573
5.0%
200462
4.2%
200351
3.5%
200248
3.3%
200121
 
1.4%

ExterQual
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size84.2 KiB
30
906 
40
488 
50
 
52
20
 
14

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2920
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row40
2nd row30
3rd row40
4th row30
5th row40
ValueCountFrequency (%)
30906
62.1%
40488
33.4%
5052
 
3.6%
2014
 
1.0%
2021-05-01T17:46:42.676700image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:42.730959image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
30906
62.1%
40488
33.4%
5052
 
3.6%
2014
 
1.0%

Most occurring characters

ValueCountFrequency (%)
01460
50.0%
3906
31.0%
4488
 
16.7%
552
 
1.8%
214
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2920
100.0%

Most frequent character per category

ValueCountFrequency (%)
01460
50.0%
3906
31.0%
4488
 
16.7%
552
 
1.8%
214
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common2920
100.0%

Most frequent character per script

ValueCountFrequency (%)
01460
50.0%
3906
31.0%
4488
 
16.7%
552
 
1.8%
214
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII2920
100.0%

Most frequent character per block

ValueCountFrequency (%)
01460
50.0%
3906
31.0%
4488
 
16.7%
552
 
1.8%
214
 
0.5%

ExterCond
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size84.2 KiB
30
1282 
40
146 
20
 
28
50
 
3
10
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2920
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row30
2nd row30
3rd row30
4th row30
5th row30
ValueCountFrequency (%)
301282
87.8%
40146
 
10.0%
2028
 
1.9%
503
 
0.2%
101
 
0.1%
2021-05-01T17:46:42.884162image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:42.939105image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
301282
87.8%
40146
 
10.0%
2028
 
1.9%
503
 
0.2%
101
 
0.1%

Most occurring characters

ValueCountFrequency (%)
01460
50.0%
31282
43.9%
4146
 
5.0%
228
 
1.0%
53
 
0.1%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2920
100.0%

Most frequent character per category

ValueCountFrequency (%)
01460
50.0%
31282
43.9%
4146
 
5.0%
228
 
1.0%
53
 
0.1%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common2920
100.0%

Most frequent character per script

ValueCountFrequency (%)
01460
50.0%
31282
43.9%
4146
 
5.0%
228
 
1.0%
53
 
0.1%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2920
100.0%

Most frequent character per block

ValueCountFrequency (%)
01460
50.0%
31282
43.9%
4146
 
5.0%
228
 
1.0%
53
 
0.1%
11
 
< 0.1%

BsmtQual
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size84.2 KiB
30
686 
40
618 
50
121 
20
 
35

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2920
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row40
2nd row40
3rd row40
4th row30
5th row40
ValueCountFrequency (%)
30686
47.0%
40618
42.3%
50121
 
8.3%
2035
 
2.4%
2021-05-01T17:46:43.097121image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:43.151413image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
30686
47.0%
40618
42.3%
50121
 
8.3%
2035
 
2.4%

Most occurring characters

ValueCountFrequency (%)
01460
50.0%
3686
23.5%
4618
21.2%
5121
 
4.1%
235
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2920
100.0%

Most frequent character per category

ValueCountFrequency (%)
01460
50.0%
3686
23.5%
4618
21.2%
5121
 
4.1%
235
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Common2920
100.0%

Most frequent character per script

ValueCountFrequency (%)
01460
50.0%
3686
23.5%
4618
21.2%
5121
 
4.1%
235
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII2920
100.0%

Most frequent character per block

ValueCountFrequency (%)
01460
50.0%
3686
23.5%
4618
21.2%
5121
 
4.1%
235
 
1.2%

BsmtCond
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size84.2 KiB
30
1348 
40
 
65
20
 
45
10
 
2

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2920
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30
2nd row30
3rd row30
4th row40
5th row30
ValueCountFrequency (%)
301348
92.3%
4065
 
4.5%
2045
 
3.1%
102
 
0.1%
2021-05-01T17:46:43.302728image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:43.357742image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
301348
92.3%
4065
 
4.5%
2045
 
3.1%
102
 
0.1%

Most occurring characters

ValueCountFrequency (%)
01460
50.0%
31348
46.2%
465
 
2.2%
245
 
1.5%
12
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2920
100.0%

Most frequent character per category

ValueCountFrequency (%)
01460
50.0%
31348
46.2%
465
 
2.2%
245
 
1.5%
12
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common2920
100.0%

Most frequent character per script

ValueCountFrequency (%)
01460
50.0%
31348
46.2%
465
 
2.2%
245
 
1.5%
12
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2920
100.0%

Most frequent character per block

ValueCountFrequency (%)
01460
50.0%
31348
46.2%
465
 
2.2%
245
 
1.5%
12
 
0.1%

BsmtExposure
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size84.2 KiB
10
953 
30
221 
40
134 
20
114 
0
 
38

Length

Max length2
Median length2
Mean length1.973972603
Min length1

Characters and Unicode

Total characters2882
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row40
3rd row20
4th row10
5th row30
ValueCountFrequency (%)
10953
65.3%
30221
 
15.1%
40134
 
9.2%
20114
 
7.8%
038
 
2.6%
2021-05-01T17:46:43.500501image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:43.557798image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
10953
65.3%
30221
 
15.1%
40134
 
9.2%
20114
 
7.8%
038
 
2.6%

Most occurring characters

ValueCountFrequency (%)
01460
50.7%
1953
33.1%
3221
 
7.7%
4134
 
4.6%
2114
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2882
100.0%

Most frequent character per category

ValueCountFrequency (%)
01460
50.7%
1953
33.1%
3221
 
7.7%
4134
 
4.6%
2114
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Common2882
100.0%

Most frequent character per script

ValueCountFrequency (%)
01460
50.7%
1953
33.1%
3221
 
7.7%
4134
 
4.6%
2114
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2882
100.0%

Most frequent character per block

ValueCountFrequency (%)
01460
50.7%
1953
33.1%
3221
 
7.7%
4134
 
4.6%
2114
 
4.0%

BsmtFinType1
Real number (ℝ≥0)

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.71232877
Minimum10
Maximum60
Zeros0
Zeros (%)0.0%
Memory size11.5 KiB
2021-05-01T17:46:43.614327image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q110
median40
Q360
95-th percentile60
Maximum60
Range50
Interquartile range (IQR)50

Descriptive statistics

Standard deviation20.70649321
Coefficient of variation (CV)0.5798135804
Kurtosis-1.645556271
Mean35.71232877
Median Absolute Deviation (MAD)20
Skewness-0.1243363168
Sum52140
Variance428.7588609
MonotocityNot monotonic
2021-05-01T17:46:43.686913image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
10467
32.0%
60418
28.6%
50220
15.1%
40148
 
10.1%
30133
 
9.1%
2074
 
5.1%
ValueCountFrequency (%)
10467
32.0%
2074
 
5.1%
30133
 
9.1%
40148
 
10.1%
50220
15.1%
60418
28.6%
ValueCountFrequency (%)
60418
28.6%
50220
15.1%
40148
 
10.1%
30133
 
9.1%
2074
 
5.1%
10467
32.0%

BsmtFinSF1
Real number (ℝ≥0)

ZEROS

Distinct637
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean443.639726
Minimum0
Maximum5644
Zeros467
Zeros (%)32.0%
Memory size11.5 KiB
2021-05-01T17:46:43.778110image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median383.5
Q3712.25
95-th percentile1274
Maximum5644
Range5644
Interquartile range (IQR)712.25

Descriptive statistics

Standard deviation456.0980908
Coefficient of variation (CV)1.028082167
Kurtosis11.11823629
Mean443.639726
Median Absolute Deviation (MAD)383.5
Skewness1.685503072
Sum647714
Variance208025.4685
MonotocityNot monotonic
2021-05-01T17:46:43.881597image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0467
32.0%
2412
 
0.8%
169
 
0.6%
205
 
0.3%
6865
 
0.3%
9365
 
0.3%
6625
 
0.3%
6165
 
0.3%
4424
 
0.3%
6554
 
0.3%
Other values (627)939
64.3%
ValueCountFrequency (%)
0467
32.0%
21
 
0.1%
169
 
0.6%
205
 
0.3%
2412
 
0.8%
251
 
0.1%
271
 
0.1%
283
 
0.2%
331
 
0.1%
351
 
0.1%
ValueCountFrequency (%)
56441
0.1%
22601
0.1%
21881
0.1%
20961
0.1%
19041
0.1%
18801
0.1%
18101
0.1%
17671
0.1%
17211
0.1%
16961
0.1%

BsmtFinType2
Real number (ℝ≥0)

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.73287671
Minimum10
Maximum60
Zeros0
Zeros (%)0.0%
Memory size11.5 KiB
2021-05-01T17:46:43.966958image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q110
median10
Q310
95-th percentile30
Maximum60
Range50
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.698586223
Coefficient of variation (CV)0.683159542
Kurtosis12.59370027
Mean12.73287671
Median Absolute Deviation (MAD)0
Skewness3.545573084
Sum18590
Variance75.66540227
MonotocityNot monotonic
2021-05-01T17:46:44.042696image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
101294
88.6%
3054
 
3.7%
2046
 
3.2%
4033
 
2.3%
5019
 
1.3%
6014
 
1.0%
ValueCountFrequency (%)
101294
88.6%
2046
 
3.2%
3054
 
3.7%
4033
 
2.3%
5019
 
1.3%
6014
 
1.0%
ValueCountFrequency (%)
6014
 
1.0%
5019
 
1.3%
4033
 
2.3%
3054
 
3.7%
2046
 
3.2%
101294
88.6%

BsmtUnfSF
Real number (ℝ≥0)

ZEROS

Distinct780
Distinct (%)53.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean567.240411
Minimum0
Maximum2336
Zeros118
Zeros (%)8.1%
Memory size11.5 KiB
2021-05-01T17:46:44.134487image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1223
median477.5
Q3808
95-th percentile1468
Maximum2336
Range2336
Interquartile range (IQR)585

Descriptive statistics

Standard deviation441.8669553
Coefficient of variation (CV)0.7789765094
Kurtosis0.4749939878
Mean567.240411
Median Absolute Deviation (MAD)288
Skewness0.9202684528
Sum828171
Variance195246.4062
MonotocityNot monotonic
2021-05-01T17:46:44.246814image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0118
 
8.1%
7289
 
0.6%
3848
 
0.5%
5727
 
0.5%
6007
 
0.5%
3007
 
0.5%
2706
 
0.4%
2806
 
0.4%
4406
 
0.4%
6256
 
0.4%
Other values (770)1280
87.7%
ValueCountFrequency (%)
0118
8.1%
141
 
0.1%
151
 
0.1%
232
 
0.1%
261
 
0.1%
291
 
0.1%
301
 
0.1%
322
 
0.1%
351
 
0.1%
364
 
0.3%
ValueCountFrequency (%)
23361
0.1%
21531
0.1%
21211
0.1%
20461
0.1%
20421
0.1%
20021
0.1%
19691
0.1%
19351
0.1%
19261
0.1%
19071
0.1%

TotalBsmtSF
Real number (ℝ≥0)

ZEROS

Distinct721
Distinct (%)49.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1057.429452
Minimum0
Maximum6110
Zeros37
Zeros (%)2.5%
Memory size11.5 KiB
2021-05-01T17:46:44.358620image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile519.3
Q1795.75
median991.5
Q31298.25
95-th percentile1753
Maximum6110
Range6110
Interquartile range (IQR)502.5

Descriptive statistics

Standard deviation438.7053245
Coefficient of variation (CV)0.4148790481
Kurtosis13.25048328
Mean1057.429452
Median Absolute Deviation (MAD)234.5
Skewness1.524254549
Sum1543847
Variance192462.3617
MonotocityNot monotonic
2021-05-01T17:46:44.464467image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
037
 
2.5%
86435
 
2.4%
67217
 
1.2%
91215
 
1.0%
104014
 
1.0%
81613
 
0.9%
72812
 
0.8%
76812
 
0.8%
78011
 
0.8%
84811
 
0.8%
Other values (711)1283
87.9%
ValueCountFrequency (%)
037
2.5%
1051
 
0.1%
1901
 
0.1%
2643
 
0.2%
2701
 
0.1%
2901
 
0.1%
3191
 
0.1%
3601
 
0.1%
3721
 
0.1%
3847
 
0.5%
ValueCountFrequency (%)
61101
0.1%
32061
0.1%
32001
0.1%
31381
0.1%
30941
0.1%
26331
0.1%
25241
0.1%
24441
0.1%
23961
0.1%
23921
0.1%

HeatingQC
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size84.2 KiB
50
741 
30
428 
40
241 
20
 
49
10
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2920
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row50
2nd row50
3rd row50
4th row40
5th row50
ValueCountFrequency (%)
50741
50.8%
30428
29.3%
40241
 
16.5%
2049
 
3.4%
101
 
0.1%
2021-05-01T17:46:44.657973image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:44.713187image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
50741
50.8%
30428
29.3%
40241
 
16.5%
2049
 
3.4%
101
 
0.1%

Most occurring characters

ValueCountFrequency (%)
01460
50.0%
5741
25.4%
3428
 
14.7%
4241
 
8.3%
249
 
1.7%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2920
100.0%

Most frequent character per category

ValueCountFrequency (%)
01460
50.0%
5741
25.4%
3428
 
14.7%
4241
 
8.3%
249
 
1.7%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common2920
100.0%

Most frequent character per script

ValueCountFrequency (%)
01460
50.0%
5741
25.4%
3428
 
14.7%
4241
 
8.3%
249
 
1.7%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2920
100.0%

Most frequent character per block

ValueCountFrequency (%)
01460
50.0%
5741
25.4%
3428
 
14.7%
4241
 
8.3%
249
 
1.7%
11
 
< 0.1%

CentralAir
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
1
1365 
0
 
95

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
11365
93.5%
095
 
6.5%
2021-05-01T17:46:44.861151image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:44.912670image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
11365
93.5%
095
 
6.5%

Most occurring characters

ValueCountFrequency (%)
11365
93.5%
095
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
11365
93.5%
095
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
11365
93.5%
095
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
11365
93.5%
095
 
6.5%

1stFlrSF
Real number (ℝ≥0)

Distinct753
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1162.626712
Minimum334
Maximum4692
Zeros0
Zeros (%)0.0%
Memory size11.5 KiB
2021-05-01T17:46:44.982043image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile672.95
Q1882
median1087
Q31391.25
95-th percentile1831.25
Maximum4692
Range4358
Interquartile range (IQR)509.25

Descriptive statistics

Standard deviation386.587738
Coefficient of variation (CV)0.3325123481
Kurtosis5.745841482
Mean1162.626712
Median Absolute Deviation (MAD)234.5
Skewness1.376756622
Sum1697435
Variance149450.0792
MonotocityNot monotonic
2021-05-01T17:46:45.089695image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86425
 
1.7%
104016
 
1.1%
91214
 
1.0%
84812
 
0.8%
89412
 
0.8%
67211
 
0.8%
6309
 
0.6%
8169
 
0.6%
9607
 
0.5%
8327
 
0.5%
Other values (743)1338
91.6%
ValueCountFrequency (%)
3341
 
0.1%
3721
 
0.1%
4381
 
0.1%
4801
 
0.1%
4837
0.5%
4951
 
0.1%
5205
0.3%
5251
 
0.1%
5261
 
0.1%
5361
 
0.1%
ValueCountFrequency (%)
46921
0.1%
32281
0.1%
31381
0.1%
28981
0.1%
26331
0.1%
25241
0.1%
25151
0.1%
24441
0.1%
24111
0.1%
24021
0.1%

GrLivArea
Real number (ℝ≥0)

Distinct861
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1515.463699
Minimum334
Maximum5642
Zeros0
Zeros (%)0.0%
Memory size11.5 KiB
2021-05-01T17:46:45.195555image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile848
Q11129.5
median1464
Q31776.75
95-th percentile2466.1
Maximum5642
Range5308
Interquartile range (IQR)647.25

Descriptive statistics

Standard deviation525.4803834
Coefficient of variation (CV)0.3467456092
Kurtosis4.895120581
Mean1515.463699
Median Absolute Deviation (MAD)326
Skewness1.366560356
Sum2212577
Variance276129.6334
MonotocityNot monotonic
2021-05-01T17:46:45.308932image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86422
 
1.5%
104014
 
1.0%
89411
 
0.8%
84810
 
0.7%
145610
 
0.7%
9129
 
0.6%
12009
 
0.6%
8168
 
0.5%
10928
 
0.5%
9877
 
0.5%
Other values (851)1352
92.6%
ValueCountFrequency (%)
3341
 
0.1%
4381
 
0.1%
4801
 
0.1%
5201
 
0.1%
6051
 
0.1%
6161
 
0.1%
6306
0.4%
6722
 
0.1%
6911
 
0.1%
6931
 
0.1%
ValueCountFrequency (%)
56421
0.1%
46761
0.1%
44761
0.1%
43161
0.1%
36271
0.1%
36081
0.1%
34931
0.1%
34471
0.1%
33951
0.1%
32791
0.1%

BsmtFullBath
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
856 
1
588 
2
 
15
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
0856
58.6%
1588
40.3%
215
 
1.0%
31
 
0.1%
2021-05-01T17:46:45.486689image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:45.540786image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0856
58.6%
1588
40.3%
215
 
1.0%
31
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0856
58.6%
1588
40.3%
215
 
1.0%
31
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
0856
58.6%
1588
40.3%
215
 
1.0%
31
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
0856
58.6%
1588
40.3%
215
 
1.0%
31
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
0856
58.6%
1588
40.3%
215
 
1.0%
31
 
0.1%

BsmtHalfBath
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1378 
1
 
80
2
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01378
94.4%
180
 
5.5%
22
 
0.1%
2021-05-01T17:46:45.694841image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:45.747723image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01378
94.4%
180
 
5.5%
22
 
0.1%

Most occurring characters

ValueCountFrequency (%)
01378
94.4%
180
 
5.5%
22
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01378
94.4%
180
 
5.5%
22
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01378
94.4%
180
 
5.5%
22
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01378
94.4%
180
 
5.5%
22
 
0.1%

FullBath
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
2
768 
1
650 
3
 
33
0
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row1
5th row2
ValueCountFrequency (%)
2768
52.6%
1650
44.5%
333
 
2.3%
09
 
0.6%
2021-05-01T17:46:45.905677image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:45.960004image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
2768
52.6%
1650
44.5%
333
 
2.3%
09
 
0.6%

Most occurring characters

ValueCountFrequency (%)
2768
52.6%
1650
44.5%
333
 
2.3%
09
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
2768
52.6%
1650
44.5%
333
 
2.3%
09
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
2768
52.6%
1650
44.5%
333
 
2.3%
09
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
2768
52.6%
1650
44.5%
333
 
2.3%
09
 
0.6%

HalfBath
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
913 
1
535 
2
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1
ValueCountFrequency (%)
0913
62.5%
1535
36.6%
212
 
0.8%
2021-05-01T17:46:46.096594image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:46.150263image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0913
62.5%
1535
36.6%
212
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0913
62.5%
1535
36.6%
212
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
0913
62.5%
1535
36.6%
212
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
0913
62.5%
1535
36.6%
212
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
0913
62.5%
1535
36.6%
212
 
0.8%

BedroomAbvGr
Real number (ℝ≥0)

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.866438356
Minimum0
Maximum8
Zeros6
Zeros (%)0.4%
Memory size11.5 KiB
2021-05-01T17:46:46.203551image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median3
Q33
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8157780441
Coefficient of variation (CV)0.2845964025
Kurtosis2.230874582
Mean2.866438356
Median Absolute Deviation (MAD)0
Skewness0.2117900963
Sum4185
Variance0.6654938173
MonotocityNot monotonic
2021-05-01T17:46:46.282659image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3804
55.1%
2358
24.5%
4213
 
14.6%
150
 
3.4%
521
 
1.4%
67
 
0.5%
06
 
0.4%
81
 
0.1%
ValueCountFrequency (%)
06
 
0.4%
150
 
3.4%
2358
24.5%
3804
55.1%
4213
 
14.6%
521
 
1.4%
67
 
0.5%
81
 
0.1%
ValueCountFrequency (%)
81
 
0.1%
67
 
0.5%
521
 
1.4%
4213
 
14.6%
3804
55.1%
2358
24.5%
150
 
3.4%
06
 
0.4%

KitchenAbvGr
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
1
1392 
2
 
65
3
 
2
0
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
11392
95.3%
265
 
4.5%
32
 
0.1%
01
 
0.1%
2021-05-01T17:46:46.460819image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:46.514844image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
11392
95.3%
265
 
4.5%
32
 
0.1%
01
 
0.1%

Most occurring characters

ValueCountFrequency (%)
11392
95.3%
265
 
4.5%
32
 
0.1%
01
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
11392
95.3%
265
 
4.5%
32
 
0.1%
01
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
11392
95.3%
265
 
4.5%
32
 
0.1%
01
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
11392
95.3%
265
 
4.5%
32
 
0.1%
01
 
0.1%

KitchenQual
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size84.2 KiB
30
735 
40
586 
50
100 
20
 
39

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2920
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row40
2nd row30
3rd row40
4th row40
5th row40
ValueCountFrequency (%)
30735
50.3%
40586
40.1%
50100
 
6.8%
2039
 
2.7%
2021-05-01T17:46:46.668622image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:46.722789image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
30735
50.3%
40586
40.1%
50100
 
6.8%
2039
 
2.7%

Most occurring characters

ValueCountFrequency (%)
01460
50.0%
3735
25.2%
4586
20.1%
5100
 
3.4%
239
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2920
100.0%

Most frequent character per category

ValueCountFrequency (%)
01460
50.0%
3735
25.2%
4586
20.1%
5100
 
3.4%
239
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Common2920
100.0%

Most frequent character per script

ValueCountFrequency (%)
01460
50.0%
3735
25.2%
4586
20.1%
5100
 
3.4%
239
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2920
100.0%

Most frequent character per block

ValueCountFrequency (%)
01460
50.0%
3735
25.2%
4586
20.1%
5100
 
3.4%
239
 
1.3%

TotRmsAbvGrd
Real number (ℝ≥0)

Distinct12
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.517808219
Minimum2
Maximum14
Zeros0
Zeros (%)0.0%
Memory size11.5 KiB
2021-05-01T17:46:46.777015image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q15
median6
Q37
95-th percentile10
Maximum14
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.625393291
Coefficient of variation (CV)0.2493772808
Kurtosis0.8807615657
Mean6.517808219
Median Absolute Deviation (MAD)1
Skewness0.6763408364
Sum9516
Variance2.641903349
MonotocityNot monotonic
2021-05-01T17:46:46.861091image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6402
27.5%
7329
22.5%
5275
18.8%
8187
12.8%
497
 
6.6%
975
 
5.1%
1047
 
3.2%
1118
 
1.2%
317
 
1.2%
1211
 
0.8%
Other values (2)2
 
0.1%
ValueCountFrequency (%)
21
 
0.1%
317
 
1.2%
497
 
6.6%
5275
18.8%
6402
27.5%
7329
22.5%
8187
12.8%
975
 
5.1%
1047
 
3.2%
1118
 
1.2%
ValueCountFrequency (%)
141
 
0.1%
1211
 
0.8%
1118
 
1.2%
1047
 
3.2%
975
 
5.1%
8187
12.8%
7329
22.5%
6402
27.5%
5275
18.8%
497
 
6.6%

Functional
Real number (ℝ≥0)

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.41780822
Minimum20
Maximum80
Zeros0
Zeros (%)0.0%
Memory size11.5 KiB
2021-05-01T17:46:46.939220image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile70
Q180
median80
Q380
95-th percentile80
Maximum80
Range60
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.676975311
Coefficient of variation (CV)0.08514616084
Kurtosis25.90042924
Mean78.41780822
Median Absolute Deviation (MAD)0
Skewness-4.912214312
Sum114490
Variance44.58199931
MonotocityNot monotonic
2021-05-01T17:46:47.012821image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
801360
93.2%
6034
 
2.3%
7031
 
2.1%
5015
 
1.0%
4014
 
1.0%
305
 
0.3%
201
 
0.1%
ValueCountFrequency (%)
201
 
0.1%
305
 
0.3%
4014
 
1.0%
5015
 
1.0%
6034
 
2.3%
7031
 
2.1%
801360
93.2%
ValueCountFrequency (%)
801360
93.2%
7031
 
2.1%
6034
 
2.3%
5015
 
1.0%
4014
 
1.0%
305
 
0.3%
201
 
0.1%

Fireplaces
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
690 
1
650 
2
115 
3
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
0690
47.3%
1650
44.5%
2115
 
7.9%
35
 
0.3%
2021-05-01T17:46:47.190527image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:47.244814image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0690
47.3%
1650
44.5%
2115
 
7.9%
35
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0690
47.3%
1650
44.5%
2115
 
7.9%
35
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
0690
47.3%
1650
44.5%
2115
 
7.9%
35
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
0690
47.3%
1650
44.5%
2115
 
7.9%
35
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
0690
47.3%
1650
44.5%
2115
 
7.9%
35
 
0.3%

FireplaceQu
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size84.2 KiB
40
1070 
30
313 
20
 
33
50
 
24
10
 
20

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2920
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row40
2nd row30
3rd row30
4th row40
5th row30
ValueCountFrequency (%)
401070
73.3%
30313
 
21.4%
2033
 
2.3%
5024
 
1.6%
1020
 
1.4%
2021-05-01T17:46:47.389648image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:47.445831image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
401070
73.3%
30313
 
21.4%
2033
 
2.3%
5024
 
1.6%
1020
 
1.4%

Most occurring characters

ValueCountFrequency (%)
01460
50.0%
41070
36.6%
3313
 
10.7%
233
 
1.1%
524
 
0.8%
120
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2920
100.0%

Most frequent character per category

ValueCountFrequency (%)
01460
50.0%
41070
36.6%
3313
 
10.7%
233
 
1.1%
524
 
0.8%
120
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common2920
100.0%

Most frequent character per script

ValueCountFrequency (%)
01460
50.0%
41070
36.6%
3313
 
10.7%
233
 
1.1%
524
 
0.8%
120
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII2920
100.0%

Most frequent character per block

ValueCountFrequency (%)
01460
50.0%
41070
36.6%
3313
 
10.7%
233
 
1.1%
524
 
0.8%
120
 
0.7%

GarageYrBlt
Real number (ℝ≥0)

Distinct97
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1979.976027
Minimum1900
Maximum2010
Zeros0
Zeros (%)0.0%
Memory size11.5 KiB
2021-05-01T17:46:47.529186image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1930
Q11962
median1984.5
Q32003
95-th percentile2007
Maximum2010
Range110
Interquartile range (IQR)41

Descriptive statistics

Standard deviation24.74968808
Coefficient of variation (CV)0.01249999381
Kurtosis-0.3625766187
Mean1979.976027
Median Absolute Deviation (MAD)19.5
Skewness-0.7192915121
Sum2890765
Variance612.5470603
MonotocityNot monotonic
2021-05-01T17:46:47.643817image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2005146
 
10.0%
200659
 
4.0%
200453
 
3.6%
200350
 
3.4%
200749
 
3.4%
197735
 
2.4%
199831
 
2.1%
199930
 
2.1%
200829
 
2.0%
197629
 
2.0%
Other values (87)949
65.0%
ValueCountFrequency (%)
19001
 
0.1%
19061
 
0.1%
19081
 
0.1%
19103
 
0.2%
19142
 
0.1%
19152
 
0.1%
19165
 
0.3%
19182
 
0.1%
192014
1.0%
19213
 
0.2%
ValueCountFrequency (%)
20103
 
0.2%
200921
 
1.4%
200829
 
2.0%
200749
 
3.4%
200659
4.0%
2005146
10.0%
200453
 
3.6%
200350
 
3.4%
200226
 
1.8%
200120
 
1.4%

GarageFinish
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size84.2 KiB
10
605 
20
422 
30
352 
0
81 

Length

Max length2
Median length2
Mean length1.944520548
Min length1

Characters and Unicode

Total characters2839
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20
2nd row20
3rd row20
4th row10
5th row20
ValueCountFrequency (%)
10605
41.4%
20422
28.9%
30352
24.1%
081
 
5.5%
2021-05-01T17:46:47.832266image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:47.888898image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
10605
41.4%
20422
28.9%
30352
24.1%
081
 
5.5%

Most occurring characters

ValueCountFrequency (%)
01460
51.4%
1605
21.3%
2422
 
14.9%
3352
 
12.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2839
100.0%

Most frequent character per category

ValueCountFrequency (%)
01460
51.4%
1605
21.3%
2422
 
14.9%
3352
 
12.4%

Most occurring scripts

ValueCountFrequency (%)
Common2839
100.0%

Most frequent character per script

ValueCountFrequency (%)
01460
51.4%
1605
21.3%
2422
 
14.9%
3352
 
12.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII2839
100.0%

Most frequent character per block

ValueCountFrequency (%)
01460
51.4%
1605
21.3%
2422
 
14.9%
3352
 
12.4%

GarageCars
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
2
824 
1
369 
3
181 
0
 
81
4
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row3
5th row3
ValueCountFrequency (%)
2824
56.4%
1369
25.3%
3181
 
12.4%
081
 
5.5%
45
 
0.3%
2021-05-01T17:46:48.035872image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:48.091063image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
2824
56.4%
1369
25.3%
3181
 
12.4%
081
 
5.5%
45
 
0.3%

Most occurring characters

ValueCountFrequency (%)
2824
56.4%
1369
25.3%
3181
 
12.4%
081
 
5.5%
45
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
2824
56.4%
1369
25.3%
3181
 
12.4%
081
 
5.5%
45
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
2824
56.4%
1369
25.3%
3181
 
12.4%
081
 
5.5%
45
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
2824
56.4%
1369
25.3%
3181
 
12.4%
081
 
5.5%
45
 
0.3%

GarageArea
Real number (ℝ≥0)

ZEROS

Distinct441
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean472.980137
Minimum0
Maximum1418
Zeros81
Zeros (%)5.5%
Memory size11.5 KiB
2021-05-01T17:46:48.172457image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1334.5
median480
Q3576
95-th percentile850.1
Maximum1418
Range1418
Interquartile range (IQR)241.5

Descriptive statistics

Standard deviation213.8048415
Coefficient of variation (CV)0.452037675
Kurtosis0.9170672023
Mean472.980137
Median Absolute Deviation (MAD)120
Skewness0.1799809067
Sum690551
Variance45712.51023
MonotocityNot monotonic
2021-05-01T17:46:48.279164image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
081
 
5.5%
44049
 
3.4%
57647
 
3.2%
24038
 
2.6%
48434
 
2.3%
52833
 
2.3%
28827
 
1.8%
40025
 
1.7%
48024
 
1.6%
26424
 
1.6%
Other values (431)1078
73.8%
ValueCountFrequency (%)
081
5.5%
1602
 
0.1%
1641
 
0.1%
1809
 
0.6%
1861
 
0.1%
1891
 
0.1%
1921
 
0.1%
1981
 
0.1%
2004
 
0.3%
2053
 
0.2%
ValueCountFrequency (%)
14181
0.1%
13901
0.1%
13561
0.1%
12481
0.1%
12201
0.1%
11661
0.1%
11341
0.1%
10691
0.1%
10531
0.1%
10522
0.1%

GarageQual
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.10273973
Minimum0
Maximum50
Zeros81
Zeros (%)5.5%
Memory size11.5 KiB
2021-05-01T17:46:48.367724image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q130
median30
Q330
95-th percentile30
Maximum50
Range50
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.228977417
Coefficient of variation (CV)0.2572339027
Kurtosis10.10813067
Mean28.10273973
Median Absolute Deviation (MAD)0
Skewness-3.228582773
Sum41030
Variance52.25811449
MonotocityNot monotonic
2021-05-01T17:46:48.442558image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
301311
89.8%
081
 
5.5%
2048
 
3.3%
4014
 
1.0%
103
 
0.2%
503
 
0.2%
ValueCountFrequency (%)
081
 
5.5%
103
 
0.2%
2048
 
3.3%
301311
89.8%
4014
 
1.0%
503
 
0.2%
ValueCountFrequency (%)
503
 
0.2%
4014
 
1.0%
301311
89.8%
2048
 
3.3%
103
 
0.2%
081
 
5.5%

GarageCond
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.0890411
Minimum0
Maximum50
Zeros81
Zeros (%)5.5%
Memory size11.5 KiB
2021-05-01T17:46:48.512121image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q130
median30
Q330
95-th percentile30
Maximum50
Range50
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.196851449
Coefficient of variation (CV)0.2562156331
Kurtosis10.32960424
Mean28.0890411
Median Absolute Deviation (MAD)0
Skewness-3.331899707
Sum41010
Variance51.79467077
MonotocityNot monotonic
2021-05-01T17:46:48.587169image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
301326
90.8%
081
 
5.5%
2035
 
2.4%
409
 
0.6%
107
 
0.5%
502
 
0.1%
ValueCountFrequency (%)
081
 
5.5%
107
 
0.5%
2035
 
2.4%
301326
90.8%
409
 
0.6%
502
 
0.1%
ValueCountFrequency (%)
502
 
0.1%
409
 
0.6%
301326
90.8%
2035
 
2.4%
107
 
0.5%
081
 
5.5%

PavedDrive
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size84.2 KiB
30
1340 
10
 
90
20
 
30

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2920
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30
2nd row30
3rd row30
4th row30
5th row30
ValueCountFrequency (%)
301340
91.8%
1090
 
6.2%
2030
 
2.1%
2021-05-01T17:46:48.758930image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:48.813143image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
301340
91.8%
1090
 
6.2%
2030
 
2.1%

Most occurring characters

ValueCountFrequency (%)
01460
50.0%
31340
45.9%
190
 
3.1%
230
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2920
100.0%

Most frequent character per category

ValueCountFrequency (%)
01460
50.0%
31340
45.9%
190
 
3.1%
230
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common2920
100.0%

Most frequent character per script

ValueCountFrequency (%)
01460
50.0%
31340
45.9%
190
 
3.1%
230
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2920
100.0%

Most frequent character per block

ValueCountFrequency (%)
01460
50.0%
31340
45.9%
190
 
3.1%
230
 
1.0%

OpenPorchSF
Real number (ℝ≥0)

ZEROS

Distinct202
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.66027397
Minimum0
Maximum547
Zeros656
Zeros (%)44.9%
Memory size11.5 KiB
2021-05-01T17:46:48.885544image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median25
Q368
95-th percentile175.05
Maximum547
Range547
Interquartile range (IQR)68

Descriptive statistics

Standard deviation66.25602768
Coefficient of variation (CV)1.419966538
Kurtosis8.490335806
Mean46.66027397
Median Absolute Deviation (MAD)25
Skewness2.36434174
Sum68124
Variance4389.861203
MonotocityNot monotonic
2021-05-01T17:46:48.990725image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0656
44.9%
3629
 
2.0%
4822
 
1.5%
2021
 
1.4%
4519
 
1.3%
4019
 
1.3%
3016
 
1.1%
2416
 
1.1%
6015
 
1.0%
2814
 
1.0%
Other values (192)633
43.4%
ValueCountFrequency (%)
0656
44.9%
41
 
0.1%
81
 
0.1%
101
 
0.1%
111
 
0.1%
123
 
0.2%
151
 
0.1%
168
 
0.5%
172
 
0.1%
185
 
0.3%
ValueCountFrequency (%)
5471
0.1%
5231
0.1%
5021
0.1%
4181
0.1%
4061
0.1%
3641
0.1%
3411
0.1%
3191
0.1%
3122
0.1%
3041
0.1%

MoSold
Real number (ℝ≥0)

Distinct12
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.321917808
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Memory size11.5 KiB
2021-05-01T17:46:49.085002image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median6
Q38
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.703626208
Coefficient of variation (CV)0.4276591836
Kurtosis-0.4041093415
Mean6.321917808
Median Absolute Deviation (MAD)2
Skewness0.2120529851
Sum9230
Variance7.309594675
MonotocityNot monotonic
2021-05-01T17:46:49.160815image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6253
17.3%
7234
16.0%
5204
14.0%
4141
9.7%
8122
8.4%
3106
7.3%
1089
 
6.1%
1179
 
5.4%
963
 
4.3%
1259
 
4.0%
Other values (2)110
7.5%
ValueCountFrequency (%)
158
 
4.0%
252
 
3.6%
3106
7.3%
4141
9.7%
5204
14.0%
6253
17.3%
7234
16.0%
8122
8.4%
963
 
4.3%
1089
 
6.1%
ValueCountFrequency (%)
1259
 
4.0%
1179
 
5.4%
1089
 
6.1%
963
 
4.3%
8122
8.4%
7234
16.0%
6253
17.3%
5204
14.0%
4141
9.7%
3106
7.3%

YrSold
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size87.1 KiB
2009
338 
2007
329 
2006
314 
2008
304 
2010
175 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters5840
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2008
2nd row2007
3rd row2008
4th row2006
5th row2008
ValueCountFrequency (%)
2009338
23.2%
2007329
22.5%
2006314
21.5%
2008304
20.8%
2010175
12.0%
2021-05-01T17:46:49.340292image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:49.397447image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
2009338
23.2%
2007329
22.5%
2006314
21.5%
2008304
20.8%
2010175
12.0%

Most occurring characters

ValueCountFrequency (%)
02920
50.0%
21460
25.0%
9338
 
5.8%
7329
 
5.6%
6314
 
5.4%
8304
 
5.2%
1175
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5840
100.0%

Most frequent character per category

ValueCountFrequency (%)
02920
50.0%
21460
25.0%
9338
 
5.8%
7329
 
5.6%
6314
 
5.4%
8304
 
5.2%
1175
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common5840
100.0%

Most frequent character per script

ValueCountFrequency (%)
02920
50.0%
21460
25.0%
9338
 
5.8%
7329
 
5.6%
6314
 
5.4%
8304
 
5.2%
1175
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5840
100.0%

Most frequent character per block

ValueCountFrequency (%)
02920
50.0%
21460
25.0%
9338
 
5.8%
7329
 
5.6%
6314
 
5.4%
8304
 
5.2%
1175
 
3.0%

SalePrice
Real number (ℝ≥0)

Distinct663
Distinct (%)45.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180921.1959
Minimum34900
Maximum755000
Zeros0
Zeros (%)0.0%
Memory size11.5 KiB
2021-05-01T17:46:49.480735image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum34900
5-th percentile88000
Q1129975
median163000
Q3214000
95-th percentile326100
Maximum755000
Range720100
Interquartile range (IQR)84025

Descriptive statistics

Standard deviation79442.50288
Coefficient of variation (CV)0.4391000319
Kurtosis6.53628186
Mean180921.1959
Median Absolute Deviation (MAD)38000
Skewness1.88287576
Sum264144946
Variance6311111264
MonotocityNot monotonic
2021-05-01T17:46:49.593377image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14000020
 
1.4%
13500017
 
1.2%
15500014
 
1.0%
14500014
 
1.0%
19000013
 
0.9%
11000013
 
0.9%
16000012
 
0.8%
11500012
 
0.8%
13000011
 
0.8%
13900011
 
0.8%
Other values (653)1323
90.6%
ValueCountFrequency (%)
349001
0.1%
353111
0.1%
379001
0.1%
393001
0.1%
400001
0.1%
520001
0.1%
525001
0.1%
550002
0.1%
559931
0.1%
585001
0.1%
ValueCountFrequency (%)
7550001
0.1%
7450001
0.1%
6250001
0.1%
6116571
0.1%
5829331
0.1%
5565811
0.1%
5550001
0.1%
5380001
0.1%
5018371
0.1%
4850001
0.1%

Utilities_was_missing
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1460 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01460
100.0%
2021-05-01T17:46:49.772444image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:49.823758image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01460
100.0%

Most occurring characters

ValueCountFrequency (%)
01460
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01460
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01460
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01460
100.0%

MSZoning_was_missing
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1460 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01460
100.0%
2021-05-01T17:46:49.953634image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:50.005369image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01460
100.0%

Most occurring characters

ValueCountFrequency (%)
01460
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01460
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01460
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01460
100.0%

GarageType_was_missing
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1379 
1
 
81

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01379
94.5%
181
 
5.5%
2021-05-01T17:46:50.140255image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:50.192797image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01379
94.5%
181
 
5.5%

Most occurring characters

ValueCountFrequency (%)
01379
94.5%
181
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01379
94.5%
181
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01379
94.5%
181
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01379
94.5%
181
 
5.5%

GarageFinish_was_missing
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1379 
1
 
81

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01379
94.5%
181
 
5.5%
2021-05-01T17:46:50.329972image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:50.382579image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01379
94.5%
181
 
5.5%

Most occurring characters

ValueCountFrequency (%)
01379
94.5%
181
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01379
94.5%
181
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01379
94.5%
181
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01379
94.5%
181
 
5.5%

GarageCars_was_missing
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1460 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01460
100.0%
2021-05-01T17:46:50.515758image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:50.567157image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01460
100.0%

Most occurring characters

ValueCountFrequency (%)
01460
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01460
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01460
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01460
100.0%

GarageArea_was_missing
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1460 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01460
100.0%
2021-05-01T17:46:50.699477image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:50.750910image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01460
100.0%

Most occurring characters

ValueCountFrequency (%)
01460
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01460
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01460
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01460
100.0%

GarageQual_was_missing
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1379 
1
 
81

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01379
94.5%
181
 
5.5%
2021-05-01T17:46:50.883908image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:50.935968image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01379
94.5%
181
 
5.5%

Most occurring characters

ValueCountFrequency (%)
01379
94.5%
181
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01379
94.5%
181
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01379
94.5%
181
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01379
94.5%
181
 
5.5%

GarageCond_was_missing
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1379 
1
 
81

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01379
94.5%
181
 
5.5%
2021-05-01T17:46:51.073328image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:51.127751image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01379
94.5%
181
 
5.5%

Most occurring characters

ValueCountFrequency (%)
01379
94.5%
181
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01379
94.5%
181
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01379
94.5%
181
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01379
94.5%
181
 
5.5%

SaleType_was_missing
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1460 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01460
100.0%
2021-05-01T17:46:51.264086image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:51.315356image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01460
100.0%

Most occurring characters

ValueCountFrequency (%)
01460
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01460
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01460
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01460
100.0%

LotShape_was_missing
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1460 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01460
100.0%
2021-05-01T17:46:51.445710image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:51.496860image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01460
100.0%

Most occurring characters

ValueCountFrequency (%)
01460
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01460
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01460
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01460
100.0%

BsmtExposure_was_missing
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1422 
1
 
38

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01422
97.4%
138
 
2.6%
2021-05-01T17:46:51.633037image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:51.686362image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01422
97.4%
138
 
2.6%

Most occurring characters

ValueCountFrequency (%)
01422
97.4%
138
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01422
97.4%
138
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01422
97.4%
138
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01422
97.4%
138
 
2.6%

MSZoning_FV
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1395 
1
 
65

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01395
95.5%
165
 
4.5%
2021-05-01T17:46:51.827789image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:51.880245image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01395
95.5%
165
 
4.5%

Most occurring characters

ValueCountFrequency (%)
01395
95.5%
165
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01395
95.5%
165
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01395
95.5%
165
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01395
95.5%
165
 
4.5%

MSZoning_RH
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1444 
1
 
16

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01444
98.9%
116
 
1.1%
2021-05-01T17:46:52.021319image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:52.075263image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01444
98.9%
116
 
1.1%

Most occurring characters

ValueCountFrequency (%)
01444
98.9%
116
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01444
98.9%
116
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01444
98.9%
116
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01444
98.9%
116
 
1.1%

MSZoning_RL
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
1
1151 
0
309 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
11151
78.8%
0309
 
21.2%
2021-05-01T17:46:52.203090image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:52.256774image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
11151
78.8%
0309
 
21.2%

Most occurring characters

ValueCountFrequency (%)
11151
78.8%
0309
 
21.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
11151
78.8%
0309
 
21.2%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
11151
78.8%
0309
 
21.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
11151
78.8%
0309
 
21.2%

MSZoning_RM
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1242 
1
218 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01242
85.1%
1218
 
14.9%
2021-05-01T17:46:52.391546image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:52.444135image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01242
85.1%
1218
 
14.9%

Most occurring characters

ValueCountFrequency (%)
01242
85.1%
1218
 
14.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01242
85.1%
1218
 
14.9%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01242
85.1%
1218
 
14.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01242
85.1%
1218
 
14.9%

LandContour_HLS
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1410 
1
 
50

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01410
96.6%
150
 
3.4%
2021-05-01T17:46:52.584497image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:52.636891image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01410
96.6%
150
 
3.4%

Most occurring characters

ValueCountFrequency (%)
01410
96.6%
150
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01410
96.6%
150
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01410
96.6%
150
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01410
96.6%
150
 
3.4%

LandContour_Low
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1424 
1
 
36

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01424
97.5%
136
 
2.5%
2021-05-01T17:46:52.779844image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:52.832521image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01424
97.5%
136
 
2.5%

Most occurring characters

ValueCountFrequency (%)
01424
97.5%
136
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01424
97.5%
136
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01424
97.5%
136
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01424
97.5%
136
 
2.5%

LandContour_Lvl
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
1
1311 
0
149 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
11311
89.8%
0149
 
10.2%
2021-05-01T17:46:52.966096image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:53.019444image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
11311
89.8%
0149
 
10.2%

Most occurring characters

ValueCountFrequency (%)
11311
89.8%
0149
 
10.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
11311
89.8%
0149
 
10.2%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
11311
89.8%
0149
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
11311
89.8%
0149
 
10.2%

LotConfig_CulDSac
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1366 
1
 
94

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01366
93.6%
194
 
6.4%
2021-05-01T17:46:53.157416image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:53.209950image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01366
93.6%
194
 
6.4%

Most occurring characters

ValueCountFrequency (%)
01366
93.6%
194
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01366
93.6%
194
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01366
93.6%
194
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01366
93.6%
194
 
6.4%

LotConfig_FR2
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1413 
1
 
47

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1
ValueCountFrequency (%)
01413
96.8%
147
 
3.2%
2021-05-01T17:46:53.351787image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:53.404313image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01413
96.8%
147
 
3.2%

Most occurring characters

ValueCountFrequency (%)
01413
96.8%
147
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01413
96.8%
147
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01413
96.8%
147
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01413
96.8%
147
 
3.2%

LotConfig_FR3
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1456 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01456
99.7%
14
 
0.3%
2021-05-01T17:46:53.544676image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:53.597929image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01456
99.7%
14
 
0.3%

Most occurring characters

ValueCountFrequency (%)
01456
99.7%
14
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01456
99.7%
14
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01456
99.7%
14
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01456
99.7%
14
 
0.3%

LotConfig_Inside
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
1
1052 
0
408 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0
ValueCountFrequency (%)
11052
72.1%
0408
 
27.9%
2021-05-01T17:46:53.735466image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:53.788766image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
11052
72.1%
0408
 
27.9%

Most occurring characters

ValueCountFrequency (%)
11052
72.1%
0408
 
27.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
11052
72.1%
0408
 
27.9%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
11052
72.1%
0408
 
27.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
11052
72.1%
0408
 
27.9%

LandSlope_Mod
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1395 
1
 
65

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01395
95.5%
165
 
4.5%
2021-05-01T17:46:53.929947image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:53.982418image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01395
95.5%
165
 
4.5%

Most occurring characters

ValueCountFrequency (%)
01395
95.5%
165
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01395
95.5%
165
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01395
95.5%
165
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01395
95.5%
165
 
4.5%

LandSlope_Sev
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1447 
1
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01447
99.1%
113
 
0.9%
2021-05-01T17:46:54.124097image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:54.178444image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01447
99.1%
113
 
0.9%

Most occurring characters

ValueCountFrequency (%)
01447
99.1%
113
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01447
99.1%
113
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01447
99.1%
113
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01447
99.1%
113
 
0.9%

Neighborhood_Blueste
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1458 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01458
99.9%
12
 
0.1%
2021-05-01T17:46:54.320477image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:54.374073image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01458
99.9%
12
 
0.1%

Most occurring characters

ValueCountFrequency (%)
01458
99.9%
12
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01458
99.9%
12
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01458
99.9%
12
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01458
99.9%
12
 
0.1%

Neighborhood_BrDale
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1444 
1
 
16

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01444
98.9%
116
 
1.1%
2021-05-01T17:46:54.513839image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:54.566304image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01444
98.9%
116
 
1.1%

Most occurring characters

ValueCountFrequency (%)
01444
98.9%
116
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01444
98.9%
116
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01444
98.9%
116
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01444
98.9%
116
 
1.1%

Neighborhood_BrkSide
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1402 
1
 
58

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01402
96.0%
158
 
4.0%
2021-05-01T17:46:55.372892image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:55.424507image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01402
96.0%
158
 
4.0%

Most occurring characters

ValueCountFrequency (%)
01402
96.0%
158
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01402
96.0%
158
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01402
96.0%
158
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01402
96.0%
158
 
4.0%

Neighborhood_ClearCr
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1432 
1
 
28

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01432
98.1%
128
 
1.9%
2021-05-01T17:46:55.561862image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:55.613712image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01432
98.1%
128
 
1.9%

Most occurring characters

ValueCountFrequency (%)
01432
98.1%
128
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01432
98.1%
128
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01432
98.1%
128
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01432
98.1%
128
 
1.9%

Neighborhood_CollgCr
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1310 
1
150 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0
ValueCountFrequency (%)
01310
89.7%
1150
 
10.3%
2021-05-01T17:46:55.746471image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:55.798620image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01310
89.7%
1150
 
10.3%

Most occurring characters

ValueCountFrequency (%)
01310
89.7%
1150
 
10.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01310
89.7%
1150
 
10.3%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01310
89.7%
1150
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01310
89.7%
1150
 
10.3%

Neighborhood_Crawfor
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1409 
1
 
51

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0
ValueCountFrequency (%)
01409
96.5%
151
 
3.5%
2021-05-01T17:46:55.939928image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:55.993032image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01409
96.5%
151
 
3.5%

Most occurring characters

ValueCountFrequency (%)
01409
96.5%
151
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01409
96.5%
151
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01409
96.5%
151
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01409
96.5%
151
 
3.5%

Neighborhood_Edwards
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1360 
1
 
100

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01360
93.2%
1100
 
6.8%
2021-05-01T17:46:56.130239image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:56.182758image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01360
93.2%
1100
 
6.8%

Most occurring characters

ValueCountFrequency (%)
01360
93.2%
1100
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01360
93.2%
1100
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01360
93.2%
1100
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01360
93.2%
1100
 
6.8%

Neighborhood_Gilbert
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1381 
1
 
79

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01381
94.6%
179
 
5.4%
2021-05-01T17:46:56.319550image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:56.372469image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01381
94.6%
179
 
5.4%

Most occurring characters

ValueCountFrequency (%)
01381
94.6%
179
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01381
94.6%
179
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01381
94.6%
179
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01381
94.6%
179
 
5.4%

Neighborhood_IDOTRR
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1423 
1
 
37

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01423
97.5%
137
 
2.5%
2021-05-01T17:46:56.513170image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:56.565813image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01423
97.5%
137
 
2.5%

Most occurring characters

ValueCountFrequency (%)
01423
97.5%
137
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01423
97.5%
137
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01423
97.5%
137
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01423
97.5%
137
 
2.5%

Neighborhood_MeadowV
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1443 
1
 
17

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01443
98.8%
117
 
1.2%
2021-05-01T17:46:56.705884image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:56.758260image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01443
98.8%
117
 
1.2%

Most occurring characters

ValueCountFrequency (%)
01443
98.8%
117
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01443
98.8%
117
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01443
98.8%
117
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01443
98.8%
117
 
1.2%

Neighborhood_Mitchel
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1411 
1
 
49

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01411
96.6%
149
 
3.4%
2021-05-01T17:46:56.900496image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:56.953258image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01411
96.6%
149
 
3.4%

Most occurring characters

ValueCountFrequency (%)
01411
96.6%
149
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01411
96.6%
149
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01411
96.6%
149
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01411
96.6%
149
 
3.4%

Neighborhood_NAmes
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1235 
1
225 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01235
84.6%
1225
 
15.4%
2021-05-01T17:46:57.087689image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:57.140506image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01235
84.6%
1225
 
15.4%

Most occurring characters

ValueCountFrequency (%)
01235
84.6%
1225
 
15.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01235
84.6%
1225
 
15.4%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01235
84.6%
1225
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01235
84.6%
1225
 
15.4%

Neighborhood_NPkVill
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1451 
1
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01451
99.4%
19
 
0.6%
2021-05-01T17:46:57.282362image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:57.335086image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01451
99.4%
19
 
0.6%

Most occurring characters

ValueCountFrequency (%)
01451
99.4%
19
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01451
99.4%
19
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01451
99.4%
19
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01451
99.4%
19
 
0.6%

Neighborhood_NWAmes
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1387 
1
 
73

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01387
95.0%
173
 
5.0%
2021-05-01T17:46:57.473015image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:57.526716image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01387
95.0%
173
 
5.0%

Most occurring characters

ValueCountFrequency (%)
01387
95.0%
173
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01387
95.0%
173
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01387
95.0%
173
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01387
95.0%
173
 
5.0%

Neighborhood_NoRidge
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1419 
1
 
41

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1
ValueCountFrequency (%)
01419
97.2%
141
 
2.8%
2021-05-01T17:46:57.667437image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:57.719811image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01419
97.2%
141
 
2.8%

Most occurring characters

ValueCountFrequency (%)
01419
97.2%
141
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01419
97.2%
141
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01419
97.2%
141
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01419
97.2%
141
 
2.8%

Neighborhood_NridgHt
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1383 
1
 
77

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01383
94.7%
177
 
5.3%
2021-05-01T17:46:57.858211image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:57.910978image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01383
94.7%
177
 
5.3%

Most occurring characters

ValueCountFrequency (%)
01383
94.7%
177
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01383
94.7%
177
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01383
94.7%
177
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01383
94.7%
177
 
5.3%

Neighborhood_OldTown
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1347 
1
 
113

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01347
92.3%
1113
 
7.7%
2021-05-01T17:46:58.050715image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:58.103359image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01347
92.3%
1113
 
7.7%

Most occurring characters

ValueCountFrequency (%)
01347
92.3%
1113
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01347
92.3%
1113
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01347
92.3%
1113
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01347
92.3%
1113
 
7.7%

Neighborhood_SWISU
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1435 
1
 
25

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01435
98.3%
125
 
1.7%
2021-05-01T17:46:58.244956image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:58.297693image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01435
98.3%
125
 
1.7%

Most occurring characters

ValueCountFrequency (%)
01435
98.3%
125
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01435
98.3%
125
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01435
98.3%
125
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01435
98.3%
125
 
1.7%

Neighborhood_Sawyer
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1386 
1
 
74

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01386
94.9%
174
 
5.1%
2021-05-01T17:46:58.436603image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:58.489186image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01386
94.9%
174
 
5.1%

Most occurring characters

ValueCountFrequency (%)
01386
94.9%
174
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01386
94.9%
174
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01386
94.9%
174
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01386
94.9%
174
 
5.1%

Neighborhood_SawyerW
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1401 
1
 
59

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01401
96.0%
159
 
4.0%
2021-05-01T17:46:58.631209image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:58.683765image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01401
96.0%
159
 
4.0%

Most occurring characters

ValueCountFrequency (%)
01401
96.0%
159
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01401
96.0%
159
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01401
96.0%
159
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01401
96.0%
159
 
4.0%

Neighborhood_Somerst
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1374 
1
 
86

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01374
94.1%
186
 
5.9%
2021-05-01T17:46:58.821450image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:58.873973image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01374
94.1%
186
 
5.9%

Most occurring characters

ValueCountFrequency (%)
01374
94.1%
186
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01374
94.1%
186
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01374
94.1%
186
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01374
94.1%
186
 
5.9%

Neighborhood_StoneBr
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1435 
1
 
25

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01435
98.3%
125
 
1.7%
2021-05-01T17:46:59.015174image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:59.068165image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01435
98.3%
125
 
1.7%

Most occurring characters

ValueCountFrequency (%)
01435
98.3%
125
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01435
98.3%
125
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01435
98.3%
125
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01435
98.3%
125
 
1.7%

Neighborhood_Timber
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1422 
1
 
38

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01422
97.4%
138
 
2.6%
2021-05-01T17:46:59.210735image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:59.263510image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01422
97.4%
138
 
2.6%

Most occurring characters

ValueCountFrequency (%)
01422
97.4%
138
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01422
97.4%
138
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01422
97.4%
138
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01422
97.4%
138
 
2.6%

Neighborhood_Veenker
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1449 
1
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01449
99.2%
111
 
0.8%
2021-05-01T17:46:59.405678image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:59.458131image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01449
99.2%
111
 
0.8%

Most occurring characters

ValueCountFrequency (%)
01449
99.2%
111
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01449
99.2%
111
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01449
99.2%
111
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01449
99.2%
111
 
0.8%

Condition1_Feedr
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1379 
1
 
81

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01379
94.5%
181
 
5.5%
2021-05-01T17:46:59.596339image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:59.649849image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01379
94.5%
181
 
5.5%

Most occurring characters

ValueCountFrequency (%)
01379
94.5%
181
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01379
94.5%
181
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01379
94.5%
181
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01379
94.5%
181
 
5.5%

Condition1_Norm
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
1
1260 
0
200 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
11260
86.3%
0200
 
13.7%
2021-05-01T17:46:59.783611image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:46:59.836511image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
11260
86.3%
0200
 
13.7%

Most occurring characters

ValueCountFrequency (%)
11260
86.3%
0200
 
13.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
11260
86.3%
0200
 
13.7%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
11260
86.3%
0200
 
13.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
11260
86.3%
0200
 
13.7%

Condition1_PosA
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1452 
1
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01452
99.5%
18
 
0.5%
2021-05-01T17:46:59.978696image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:00.031712image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01452
99.5%
18
 
0.5%

Most occurring characters

ValueCountFrequency (%)
01452
99.5%
18
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01452
99.5%
18
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01452
99.5%
18
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01452
99.5%
18
 
0.5%

Condition1_PosN
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1441 
1
 
19

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01441
98.7%
119
 
1.3%
2021-05-01T17:47:00.174094image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:00.227028image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01441
98.7%
119
 
1.3%

Most occurring characters

ValueCountFrequency (%)
01441
98.7%
119
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01441
98.7%
119
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01441
98.7%
119
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01441
98.7%
119
 
1.3%

Condition1_RRAe
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1449 
1
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01449
99.2%
111
 
0.8%
2021-05-01T17:47:00.367321image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:00.420034image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01449
99.2%
111
 
0.8%

Most occurring characters

ValueCountFrequency (%)
01449
99.2%
111
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01449
99.2%
111
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01449
99.2%
111
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01449
99.2%
111
 
0.8%

Condition1_RRAn
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1434 
1
 
26

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01434
98.2%
126
 
1.8%
2021-05-01T17:47:00.560488image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:00.613018image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01434
98.2%
126
 
1.8%

Most occurring characters

ValueCountFrequency (%)
01434
98.2%
126
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01434
98.2%
126
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01434
98.2%
126
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01434
98.2%
126
 
1.8%

Condition1_RRNe
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1458 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01458
99.9%
12
 
0.1%
2021-05-01T17:47:00.755104image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:00.808195image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01458
99.9%
12
 
0.1%

Most occurring characters

ValueCountFrequency (%)
01458
99.9%
12
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01458
99.9%
12
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01458
99.9%
12
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01458
99.9%
12
 
0.1%

Condition1_RRNn
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1455 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01455
99.7%
15
 
0.3%
2021-05-01T17:47:00.948169image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:01.000837image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01455
99.7%
15
 
0.3%

Most occurring characters

ValueCountFrequency (%)
01455
99.7%
15
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01455
99.7%
15
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01455
99.7%
15
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01455
99.7%
15
 
0.3%

Condition2_Feedr
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1454 
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01454
99.6%
16
 
0.4%
2021-05-01T17:47:01.142987image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:01.197724image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01454
99.6%
16
 
0.4%

Most occurring characters

ValueCountFrequency (%)
01454
99.6%
16
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01454
99.6%
16
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01454
99.6%
16
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01454
99.6%
16
 
0.4%

Condition2_Norm
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
1
1445 
0
 
15

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
11445
99.0%
015
 
1.0%
2021-05-01T17:47:01.338674image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:01.393506image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
11445
99.0%
015
 
1.0%

Most occurring characters

ValueCountFrequency (%)
11445
99.0%
015
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
11445
99.0%
015
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
11445
99.0%
015
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
11445
99.0%
015
 
1.0%

Condition2_PosA
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1459 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01459
99.9%
11
 
0.1%
2021-05-01T17:47:01.536579image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:01.589387image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Most occurring characters

ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Condition2_PosN
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1458 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01458
99.9%
12
 
0.1%
2021-05-01T17:47:01.730299image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:01.783534image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01458
99.9%
12
 
0.1%

Most occurring characters

ValueCountFrequency (%)
01458
99.9%
12
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01458
99.9%
12
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01458
99.9%
12
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01458
99.9%
12
 
0.1%

Condition2_RRAe
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1459 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01459
99.9%
11
 
0.1%
2021-05-01T17:47:01.924404image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:01.978020image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Most occurring characters

ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Condition2_RRAn
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1459 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01459
99.9%
11
 
0.1%
2021-05-01T17:47:02.119844image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:02.172642image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Most occurring characters

ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Condition2_RRNn
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1458 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01458
99.9%
12
 
0.1%
2021-05-01T17:47:02.314708image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:02.368368image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01458
99.9%
12
 
0.1%

Most occurring characters

ValueCountFrequency (%)
01458
99.9%
12
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01458
99.9%
12
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01458
99.9%
12
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01458
99.9%
12
 
0.1%

BldgType_2fmCon
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1429 
1
 
31

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01429
97.9%
131
 
2.1%
2021-05-01T17:47:02.509611image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:02.563042image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01429
97.9%
131
 
2.1%

Most occurring characters

ValueCountFrequency (%)
01429
97.9%
131
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01429
97.9%
131
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01429
97.9%
131
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01429
97.9%
131
 
2.1%

BldgType_Duplex
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1408 
1
 
52

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01408
96.4%
152
 
3.6%
2021-05-01T17:47:02.705191image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:02.757860image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01408
96.4%
152
 
3.6%

Most occurring characters

ValueCountFrequency (%)
01408
96.4%
152
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01408
96.4%
152
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01408
96.4%
152
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01408
96.4%
152
 
3.6%

BldgType_Twnhs
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1417 
1
 
43

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01417
97.1%
143
 
2.9%
2021-05-01T17:47:02.900393image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:02.955596image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01417
97.1%
143
 
2.9%

Most occurring characters

ValueCountFrequency (%)
01417
97.1%
143
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01417
97.1%
143
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01417
97.1%
143
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01417
97.1%
143
 
2.9%

BldgType_TwnhsE
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1346 
1
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01346
92.2%
1114
 
7.8%
2021-05-01T17:47:03.094427image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:03.148152image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01346
92.2%
1114
 
7.8%

Most occurring characters

ValueCountFrequency (%)
01346
92.2%
1114
 
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01346
92.2%
1114
 
7.8%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01346
92.2%
1114
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01346
92.2%
1114
 
7.8%

HouseStyle_1.5Unf
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1446 
1
 
14

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01446
99.0%
114
 
1.0%
2021-05-01T17:47:03.292606image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:03.346836image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01446
99.0%
114
 
1.0%

Most occurring characters

ValueCountFrequency (%)
01446
99.0%
114
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01446
99.0%
114
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01446
99.0%
114
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01446
99.0%
114
 
1.0%

HouseStyle_1Story
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
734 
1
726 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0734
50.3%
1726
49.7%
2021-05-01T17:47:03.491870image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:03.544398image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0734
50.3%
1726
49.7%

Most occurring characters

ValueCountFrequency (%)
0734
50.3%
1726
49.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
0734
50.3%
1726
49.7%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
0734
50.3%
1726
49.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
0734
50.3%
1726
49.7%

HouseStyle_2.5Fin
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1452 
1
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01452
99.5%
18
 
0.5%
2021-05-01T17:47:03.685844image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:03.738490image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01452
99.5%
18
 
0.5%

Most occurring characters

ValueCountFrequency (%)
01452
99.5%
18
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01452
99.5%
18
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01452
99.5%
18
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01452
99.5%
18
 
0.5%

HouseStyle_2.5Unf
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1449 
1
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01449
99.2%
111
 
0.8%
2021-05-01T17:47:03.880141image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:03.933343image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01449
99.2%
111
 
0.8%

Most occurring characters

ValueCountFrequency (%)
01449
99.2%
111
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01449
99.2%
111
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01449
99.2%
111
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01449
99.2%
111
 
0.8%

HouseStyle_2Story
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1015 
1
445 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
01015
69.5%
1445
30.5%
2021-05-01T17:47:04.068398image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:04.121086image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01015
69.5%
1445
30.5%

Most occurring characters

ValueCountFrequency (%)
01015
69.5%
1445
30.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01015
69.5%
1445
30.5%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01015
69.5%
1445
30.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01015
69.5%
1445
30.5%

HouseStyle_SFoyer
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1423 
1
 
37

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01423
97.5%
137
 
2.5%
2021-05-01T17:47:04.264042image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:04.317393image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01423
97.5%
137
 
2.5%

Most occurring characters

ValueCountFrequency (%)
01423
97.5%
137
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01423
97.5%
137
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01423
97.5%
137
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01423
97.5%
137
 
2.5%

HouseStyle_SLvl
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1395 
1
 
65

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01395
95.5%
165
 
4.5%
2021-05-01T17:47:04.460576image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:04.513841image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01395
95.5%
165
 
4.5%

Most occurring characters

ValueCountFrequency (%)
01395
95.5%
165
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01395
95.5%
165
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01395
95.5%
165
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01395
95.5%
165
 
4.5%

RoofStyle_Gable
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
1
1141 
0
319 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
11141
78.2%
0319
 
21.8%
2021-05-01T17:47:04.640596image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:04.693370image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
11141
78.2%
0319
 
21.8%

Most occurring characters

ValueCountFrequency (%)
11141
78.2%
0319
 
21.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
11141
78.2%
0319
 
21.8%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
11141
78.2%
0319
 
21.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
11141
78.2%
0319
 
21.8%

RoofStyle_Gambrel
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1449 
1
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01449
99.2%
111
 
0.8%
2021-05-01T17:47:04.833878image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:04.887127image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01449
99.2%
111
 
0.8%

Most occurring characters

ValueCountFrequency (%)
01449
99.2%
111
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01449
99.2%
111
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01449
99.2%
111
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01449
99.2%
111
 
0.8%

RoofStyle_Hip
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1174 
1
286 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01174
80.4%
1286
 
19.6%
2021-05-01T17:47:05.020280image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:05.078645image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01174
80.4%
1286
 
19.6%

Most occurring characters

ValueCountFrequency (%)
01174
80.4%
1286
 
19.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01174
80.4%
1286
 
19.6%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01174
80.4%
1286
 
19.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01174
80.4%
1286
 
19.6%

RoofStyle_Mansard
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1453 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01453
99.5%
17
 
0.5%
2021-05-01T17:47:05.270720image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:05.342158image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01453
99.5%
17
 
0.5%

Most occurring characters

ValueCountFrequency (%)
01453
99.5%
17
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01453
99.5%
17
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01453
99.5%
17
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01453
99.5%
17
 
0.5%

RoofStyle_Shed
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1458 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01458
99.9%
12
 
0.1%
2021-05-01T17:47:05.480899image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:05.532698image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01458
99.9%
12
 
0.1%

Most occurring characters

ValueCountFrequency (%)
01458
99.9%
12
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01458
99.9%
12
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01458
99.9%
12
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01458
99.9%
12
 
0.1%

RoofMatl_CompShg
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
1
1434 
0
 
26

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
11434
98.2%
026
 
1.8%
2021-05-01T17:47:05.672701image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:05.724698image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
11434
98.2%
026
 
1.8%

Most occurring characters

ValueCountFrequency (%)
11434
98.2%
026
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
11434
98.2%
026
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
11434
98.2%
026
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
11434
98.2%
026
 
1.8%

RoofMatl_Membran
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1459 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01459
99.9%
11
 
0.1%
2021-05-01T17:47:05.862712image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:05.914852image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Most occurring characters

ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

RoofMatl_Metal
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1459 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01459
99.9%
11
 
0.1%
2021-05-01T17:47:06.058366image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:06.110750image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Most occurring characters

ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

RoofMatl_Roll
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1459 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01459
99.9%
11
 
0.1%
2021-05-01T17:47:06.250685image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:06.302533image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Most occurring characters

ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

RoofMatl_Tar&Grv
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1449 
1
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01449
99.2%
111
 
0.8%
2021-05-01T17:47:06.440481image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:06.492548image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01449
99.2%
111
 
0.8%

Most occurring characters

ValueCountFrequency (%)
01449
99.2%
111
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01449
99.2%
111
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01449
99.2%
111
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01449
99.2%
111
 
0.8%

RoofMatl_WdShake
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1455 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01455
99.7%
15
 
0.3%
2021-05-01T17:47:06.631788image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:06.683784image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01455
99.7%
15
 
0.3%

Most occurring characters

ValueCountFrequency (%)
01455
99.7%
15
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01455
99.7%
15
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01455
99.7%
15
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01455
99.7%
15
 
0.3%

RoofMatl_WdShngl
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1454 
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01454
99.6%
16
 
0.4%
2021-05-01T17:47:06.824125image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:06.876294image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01454
99.6%
16
 
0.4%

Most occurring characters

ValueCountFrequency (%)
01454
99.6%
16
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01454
99.6%
16
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01454
99.6%
16
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01454
99.6%
16
 
0.4%

Exterior1st_AsphShn
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1459 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01459
99.9%
11
 
0.1%
2021-05-01T17:47:07.013638image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:07.067528image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Most occurring characters

ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Exterior1st_BrkComm
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1458 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01458
99.9%
12
 
0.1%
2021-05-01T17:47:07.206689image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:07.258677image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01458
99.9%
12
 
0.1%

Most occurring characters

ValueCountFrequency (%)
01458
99.9%
12
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01458
99.9%
12
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01458
99.9%
12
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01458
99.9%
12
 
0.1%

Exterior1st_BrkFace
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1410 
1
 
50

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01410
96.6%
150
 
3.4%
2021-05-01T17:47:07.400123image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:07.452489image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01410
96.6%
150
 
3.4%

Most occurring characters

ValueCountFrequency (%)
01410
96.6%
150
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01410
96.6%
150
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01410
96.6%
150
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01410
96.6%
150
 
3.4%

Exterior1st_CBlock
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1459 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01459
99.9%
11
 
0.1%
2021-05-01T17:47:07.593102image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:07.645221image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Most occurring characters

ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Exterior1st_CemntBd
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1399 
1
 
61

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01399
95.8%
161
 
4.2%
2021-05-01T17:47:07.782522image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:07.834345image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01399
95.8%
161
 
4.2%

Most occurring characters

ValueCountFrequency (%)
01399
95.8%
161
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01399
95.8%
161
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01399
95.8%
161
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01399
95.8%
161
 
4.2%

Exterior1st_HdBoard
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1238 
1
222 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01238
84.8%
1222
 
15.2%
2021-05-01T17:47:07.968533image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:08.021003image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01238
84.8%
1222
 
15.2%

Most occurring characters

ValueCountFrequency (%)
01238
84.8%
1222
 
15.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01238
84.8%
1222
 
15.2%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01238
84.8%
1222
 
15.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01238
84.8%
1222
 
15.2%

Exterior1st_ImStucc
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1459 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01459
99.9%
11
 
0.1%
2021-05-01T17:47:08.163627image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:08.215951image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Most occurring characters

ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Exterior1st_MetalSd
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1240 
1
220 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01240
84.9%
1220
 
15.1%
2021-05-01T17:47:08.348663image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:08.401074image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01240
84.9%
1220
 
15.1%

Most occurring characters

ValueCountFrequency (%)
01240
84.9%
1220
 
15.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01240
84.9%
1220
 
15.1%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01240
84.9%
1220
 
15.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01240
84.9%
1220
 
15.1%

Exterior1st_Plywood
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1352 
1
 
108

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01352
92.6%
1108
 
7.4%
2021-05-01T17:47:08.536919image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:08.591156image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01352
92.6%
1108
 
7.4%

Most occurring characters

ValueCountFrequency (%)
01352
92.6%
1108
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01352
92.6%
1108
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01352
92.6%
1108
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01352
92.6%
1108
 
7.4%

Exterior1st_Stone
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1458 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01458
99.9%
12
 
0.1%
2021-05-01T17:47:08.730925image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:08.783134image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01458
99.9%
12
 
0.1%

Most occurring characters

ValueCountFrequency (%)
01458
99.9%
12
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01458
99.9%
12
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01458
99.9%
12
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01458
99.9%
12
 
0.1%

Exterior1st_Stucco
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1435 
1
 
25

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01435
98.3%
125
 
1.7%
2021-05-01T17:47:08.921245image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:08.973242image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01435
98.3%
125
 
1.7%

Most occurring characters

ValueCountFrequency (%)
01435
98.3%
125
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01435
98.3%
125
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01435
98.3%
125
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01435
98.3%
125
 
1.7%

Exterior1st_VinylSd
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
945 
1
515 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1
ValueCountFrequency (%)
0945
64.7%
1515
35.3%
2021-05-01T17:47:09.103624image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:09.156816image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0945
64.7%
1515
35.3%

Most occurring characters

ValueCountFrequency (%)
0945
64.7%
1515
35.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
0945
64.7%
1515
35.3%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
0945
64.7%
1515
35.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
0945
64.7%
1515
35.3%

Exterior1st_Wd Sdng
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1254 
1
206 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0
ValueCountFrequency (%)
01254
85.9%
1206
 
14.1%
2021-05-01T17:47:09.292753image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:09.344853image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01254
85.9%
1206
 
14.1%

Most occurring characters

ValueCountFrequency (%)
01254
85.9%
1206
 
14.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01254
85.9%
1206
 
14.1%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01254
85.9%
1206
 
14.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01254
85.9%
1206
 
14.1%

Exterior1st_WdShing
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1434 
1
 
26

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01434
98.2%
126
 
1.8%
2021-05-01T17:47:09.483097image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:09.535218image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01434
98.2%
126
 
1.8%

Most occurring characters

ValueCountFrequency (%)
01434
98.2%
126
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01434
98.2%
126
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01434
98.2%
126
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01434
98.2%
126
 
1.8%

Exterior2nd_AsphShn
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1457 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01457
99.8%
13
 
0.2%
2021-05-01T17:47:09.673500image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:09.727524image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01457
99.8%
13
 
0.2%

Most occurring characters

ValueCountFrequency (%)
01457
99.8%
13
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01457
99.8%
13
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01457
99.8%
13
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01457
99.8%
13
 
0.2%

Exterior2nd_Brk Cmn
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1453 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01453
99.5%
17
 
0.5%
2021-05-01T17:47:09.868003image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:09.920369image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01453
99.5%
17
 
0.5%

Most occurring characters

ValueCountFrequency (%)
01453
99.5%
17
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01453
99.5%
17
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01453
99.5%
17
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01453
99.5%
17
 
0.5%

Exterior2nd_BrkFace
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1435 
1
 
25

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01435
98.3%
125
 
1.7%
2021-05-01T17:47:10.060216image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:10.112575image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01435
98.3%
125
 
1.7%

Most occurring characters

ValueCountFrequency (%)
01435
98.3%
125
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01435
98.3%
125
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01435
98.3%
125
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01435
98.3%
125
 
1.7%

Exterior2nd_CBlock
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1459 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01459
99.9%
11
 
0.1%
2021-05-01T17:47:10.253297image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:10.305441image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Most occurring characters

ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Exterior2nd_CmentBd
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1400 
1
 
60

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01400
95.9%
160
 
4.1%
2021-05-01T17:47:10.446106image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:10.498410image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01400
95.9%
160
 
4.1%

Most occurring characters

ValueCountFrequency (%)
01400
95.9%
160
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01400
95.9%
160
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01400
95.9%
160
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01400
95.9%
160
 
4.1%

Exterior2nd_HdBoard
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1253 
1
207 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01253
85.8%
1207
 
14.2%
2021-05-01T17:47:10.630660image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:10.682758image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01253
85.8%
1207
 
14.2%

Most occurring characters

ValueCountFrequency (%)
01253
85.8%
1207
 
14.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01253
85.8%
1207
 
14.2%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01253
85.8%
1207
 
14.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01253
85.8%
1207
 
14.2%

Exterior2nd_ImStucc
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1450 
1
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01450
99.3%
110
 
0.7%
2021-05-01T17:47:10.823102image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:10.874927image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01450
99.3%
110
 
0.7%

Most occurring characters

ValueCountFrequency (%)
01450
99.3%
110
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01450
99.3%
110
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01450
99.3%
110
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01450
99.3%
110
 
0.7%

Exterior2nd_MetalSd
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1246 
1
214 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01246
85.3%
1214
 
14.7%
2021-05-01T17:47:11.009238image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:11.061474image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01246
85.3%
1214
 
14.7%

Most occurring characters

ValueCountFrequency (%)
01246
85.3%
1214
 
14.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01246
85.3%
1214
 
14.7%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01246
85.3%
1214
 
14.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01246
85.3%
1214
 
14.7%

Exterior2nd_Other
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1459 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01459
99.9%
11
 
0.1%
2021-05-01T17:47:11.202051image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:11.254540image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Most occurring characters

ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Exterior2nd_Plywood
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1318 
1
142 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01318
90.3%
1142
 
9.7%
2021-05-01T17:47:11.388296image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:11.440470image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01318
90.3%
1142
 
9.7%

Most occurring characters

ValueCountFrequency (%)
01318
90.3%
1142
 
9.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01318
90.3%
1142
 
9.7%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01318
90.3%
1142
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01318
90.3%
1142
 
9.7%

Exterior2nd_Stone
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1455 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01455
99.7%
15
 
0.3%
2021-05-01T17:47:11.579736image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:11.631931image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01455
99.7%
15
 
0.3%

Most occurring characters

ValueCountFrequency (%)
01455
99.7%
15
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01455
99.7%
15
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01455
99.7%
15
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01455
99.7%
15
 
0.3%

Exterior2nd_Stucco
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1434 
1
 
26

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01434
98.2%
126
 
1.8%
2021-05-01T17:47:11.769487image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:11.823450image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01434
98.2%
126
 
1.8%

Most occurring characters

ValueCountFrequency (%)
01434
98.2%
126
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01434
98.2%
126
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01434
98.2%
126
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01434
98.2%
126
 
1.8%

Exterior2nd_VinylSd
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
956 
1
504 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1
ValueCountFrequency (%)
0956
65.5%
1504
34.5%
2021-05-01T17:47:11.952774image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:12.005203image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0956
65.5%
1504
34.5%

Most occurring characters

ValueCountFrequency (%)
0956
65.5%
1504
34.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
0956
65.5%
1504
34.5%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
0956
65.5%
1504
34.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
0956
65.5%
1504
34.5%

Exterior2nd_Wd Sdng
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1263 
1
197 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01263
86.5%
1197
 
13.5%
2021-05-01T17:47:12.138578image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:12.192977image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01263
86.5%
1197
 
13.5%

Most occurring characters

ValueCountFrequency (%)
01263
86.5%
1197
 
13.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01263
86.5%
1197
 
13.5%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01263
86.5%
1197
 
13.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01263
86.5%
1197
 
13.5%

Exterior2nd_Wd Shng
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1422 
1
 
38

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0
ValueCountFrequency (%)
01422
97.4%
138
 
2.6%
2021-05-01T17:47:12.332623image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:12.386272image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01422
97.4%
138
 
2.6%

Most occurring characters

ValueCountFrequency (%)
01422
97.4%
138
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01422
97.4%
138
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01422
97.4%
138
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01422
97.4%
138
 
2.6%

MasVnrType_BrkFace
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1015 
1
445 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1
ValueCountFrequency (%)
01015
69.5%
1445
30.5%
2021-05-01T17:47:12.519053image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:12.571092image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01015
69.5%
1445
30.5%

Most occurring characters

ValueCountFrequency (%)
01015
69.5%
1445
30.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01015
69.5%
1445
30.5%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01015
69.5%
1445
30.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01015
69.5%
1445
30.5%

MasVnrType_None
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
1
872 
0
588 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0
ValueCountFrequency (%)
1872
59.7%
0588
40.3%
2021-05-01T17:47:12.703396image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:13.634916image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1872
59.7%
0588
40.3%

Most occurring characters

ValueCountFrequency (%)
1872
59.7%
0588
40.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
1872
59.7%
0588
40.3%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
1872
59.7%
0588
40.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
1872
59.7%
0588
40.3%

MasVnrType_Stone
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1332 
1
 
128

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01332
91.2%
1128
 
8.8%
2021-05-01T17:47:13.759356image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:13.809356image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01332
91.2%
1128
 
8.8%

Most occurring characters

ValueCountFrequency (%)
01332
91.2%
1128
 
8.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01332
91.2%
1128
 
8.8%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01332
91.2%
1128
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01332
91.2%
1128
 
8.8%

Foundation_CBlock
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
826 
1
634 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0826
56.6%
1634
43.4%
2021-05-01T17:47:13.953638image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:14.007821image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0826
56.6%
1634
43.4%

Most occurring characters

ValueCountFrequency (%)
0826
56.6%
1634
43.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
0826
56.6%
1634
43.4%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
0826
56.6%
1634
43.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
0826
56.6%
1634
43.4%

Foundation_PConc
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
813 
1
647 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1
ValueCountFrequency (%)
0813
55.7%
1647
44.3%
2021-05-01T17:47:14.143814image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:14.195931image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0813
55.7%
1647
44.3%

Most occurring characters

ValueCountFrequency (%)
0813
55.7%
1647
44.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
0813
55.7%
1647
44.3%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
0813
55.7%
1647
44.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
0813
55.7%
1647
44.3%

Foundation_Slab
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1436 
1
 
24

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01436
98.4%
124
 
1.6%
2021-05-01T17:47:14.334206image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:14.387101image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01436
98.4%
124
 
1.6%

Most occurring characters

ValueCountFrequency (%)
01436
98.4%
124
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01436
98.4%
124
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01436
98.4%
124
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01436
98.4%
124
 
1.6%

Foundation_Stone
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1454 
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01454
99.6%
16
 
0.4%
2021-05-01T17:47:14.528259image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:14.582395image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01454
99.6%
16
 
0.4%

Most occurring characters

ValueCountFrequency (%)
01454
99.6%
16
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01454
99.6%
16
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01454
99.6%
16
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01454
99.6%
16
 
0.4%

Foundation_Wood
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1457 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01457
99.8%
13
 
0.2%
2021-05-01T17:47:14.720518image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:14.772431image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01457
99.8%
13
 
0.2%

Most occurring characters

ValueCountFrequency (%)
01457
99.8%
13
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01457
99.8%
13
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01457
99.8%
13
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01457
99.8%
13
 
0.2%

Heating_GasA
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
1
1428 
0
 
32

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
11428
97.8%
032
 
2.2%
2021-05-01T17:47:14.910230image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:14.962505image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
11428
97.8%
032
 
2.2%

Most occurring characters

ValueCountFrequency (%)
11428
97.8%
032
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
11428
97.8%
032
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
11428
97.8%
032
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
11428
97.8%
032
 
2.2%

Heating_GasW
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1442 
1
 
18

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01442
98.8%
118
 
1.2%
2021-05-01T17:47:15.104572image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:15.157619image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01442
98.8%
118
 
1.2%

Most occurring characters

ValueCountFrequency (%)
01442
98.8%
118
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01442
98.8%
118
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01442
98.8%
118
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01442
98.8%
118
 
1.2%

Heating_Grav
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1453 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01453
99.5%
17
 
0.5%
2021-05-01T17:47:15.297249image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:15.349603image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01453
99.5%
17
 
0.5%

Most occurring characters

ValueCountFrequency (%)
01453
99.5%
17
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01453
99.5%
17
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01453
99.5%
17
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01453
99.5%
17
 
0.5%

Heating_OthW
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1458 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01458
99.9%
12
 
0.1%
2021-05-01T17:47:15.487496image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:15.541644image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01458
99.9%
12
 
0.1%

Most occurring characters

ValueCountFrequency (%)
01458
99.9%
12
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01458
99.9%
12
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01458
99.9%
12
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01458
99.9%
12
 
0.1%

Heating_Wall
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1456 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01456
99.7%
14
 
0.3%
2021-05-01T17:47:15.679225image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:15.731031image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01456
99.7%
14
 
0.3%

Most occurring characters

ValueCountFrequency (%)
01456
99.7%
14
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01456
99.7%
14
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01456
99.7%
14
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01456
99.7%
14
 
0.3%

Electrical_FuseF
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1433 
1
 
27

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01433
98.2%
127
 
1.8%
2021-05-01T17:47:15.870498image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:15.922248image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01433
98.2%
127
 
1.8%

Most occurring characters

ValueCountFrequency (%)
01433
98.2%
127
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01433
98.2%
127
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01433
98.2%
127
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01433
98.2%
127
 
1.8%

Electrical_FuseP
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1457 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01457
99.8%
13
 
0.2%
2021-05-01T17:47:16.062845image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:16.115421image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01457
99.8%
13
 
0.2%

Most occurring characters

ValueCountFrequency (%)
01457
99.8%
13
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01457
99.8%
13
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01457
99.8%
13
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01457
99.8%
13
 
0.2%

Electrical_Mix
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1459 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01459
99.9%
11
 
0.1%
2021-05-01T17:47:16.254004image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:16.306172image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Most occurring characters

ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01459
99.9%
11
 
0.1%

Electrical_SBrkr
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
1
1335 
0
 
125

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
11335
91.4%
0125
 
8.6%
2021-05-01T17:47:16.440477image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:16.492685image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
11335
91.4%
0125
 
8.6%

Most occurring characters

ValueCountFrequency (%)
11335
91.4%
0125
 
8.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
11335
91.4%
0125
 
8.6%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
11335
91.4%
0125
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
11335
91.4%
0125
 
8.6%

GarageType_Attchd
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
1
870 
0
590 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1
ValueCountFrequency (%)
1870
59.6%
0590
40.4%
2021-05-01T17:47:16.628467image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:16.680561image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1870
59.6%
0590
40.4%

Most occurring characters

ValueCountFrequency (%)
1870
59.6%
0590
40.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
1870
59.6%
0590
40.4%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
1870
59.6%
0590
40.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
1870
59.6%
0590
40.4%

GarageType_Basment
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1441 
1
 
19

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01441
98.7%
119
 
1.3%
2021-05-01T17:47:16.818777image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:16.870800image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01441
98.7%
119
 
1.3%

Most occurring characters

ValueCountFrequency (%)
01441
98.7%
119
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01441
98.7%
119
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01441
98.7%
119
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01441
98.7%
119
 
1.3%

GarageType_BuiltIn
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1372 
1
 
88

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01372
94.0%
188
 
6.0%
2021-05-01T17:47:17.008272image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:17.060437image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01372
94.0%
188
 
6.0%

Most occurring characters

ValueCountFrequency (%)
01372
94.0%
188
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01372
94.0%
188
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01372
94.0%
188
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01372
94.0%
188
 
6.0%

GarageType_CarPort
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1451 
1
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01451
99.4%
19
 
0.6%
2021-05-01T17:47:17.201689image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:17.253798image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01451
99.4%
19
 
0.6%

Most occurring characters

ValueCountFrequency (%)
01451
99.4%
19
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01451
99.4%
19
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01451
99.4%
19
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01451
99.4%
19
 
0.6%

GarageType_Detchd
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1073 
1
387 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0
ValueCountFrequency (%)
01073
73.5%
1387
 
26.5%
2021-05-01T17:47:17.393667image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:17.445926image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01073
73.5%
1387
 
26.5%

Most occurring characters

ValueCountFrequency (%)
01073
73.5%
1387
 
26.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01073
73.5%
1387
 
26.5%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01073
73.5%
1387
 
26.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01073
73.5%
1387
 
26.5%

GarageType_NA
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1379 
1
 
81

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01379
94.5%
181
 
5.5%
2021-05-01T17:47:17.583284image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:17.637014image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01379
94.5%
181
 
5.5%

Most occurring characters

ValueCountFrequency (%)
01379
94.5%
181
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01379
94.5%
181
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01379
94.5%
181
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01379
94.5%
181
 
5.5%

SaleType_CWD
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1456 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01456
99.7%
14
 
0.3%
2021-05-01T17:47:17.775376image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:17.827175image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01456
99.7%
14
 
0.3%

Most occurring characters

ValueCountFrequency (%)
01456
99.7%
14
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01456
99.7%
14
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01456
99.7%
14
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01456
99.7%
14
 
0.3%

SaleType_Con
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1458 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01458
99.9%
12
 
0.1%
2021-05-01T17:47:17.964782image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:18.016964image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01458
99.9%
12
 
0.1%

Most occurring characters

ValueCountFrequency (%)
01458
99.9%
12
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01458
99.9%
12
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01458
99.9%
12
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01458
99.9%
12
 
0.1%

SaleType_ConLD
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1451 
1
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01451
99.4%
19
 
0.6%
2021-05-01T17:47:18.157570image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:18.212891image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01451
99.4%
19
 
0.6%

Most occurring characters

ValueCountFrequency (%)
01451
99.4%
19
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01451
99.4%
19
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01451
99.4%
19
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01451
99.4%
19
 
0.6%

SaleType_ConLI
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1455 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01455
99.7%
15
 
0.3%
2021-05-01T17:47:18.351430image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:18.403639image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01455
99.7%
15
 
0.3%

Most occurring characters

ValueCountFrequency (%)
01455
99.7%
15
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01455
99.7%
15
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01455
99.7%
15
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01455
99.7%
15
 
0.3%

SaleType_ConLw
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1455 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01455
99.7%
15
 
0.3%
2021-05-01T17:47:18.541496image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:18.593426image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01455
99.7%
15
 
0.3%

Most occurring characters

ValueCountFrequency (%)
01455
99.7%
15
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01455
99.7%
15
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01455
99.7%
15
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01455
99.7%
15
 
0.3%

SaleType_New
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1338 
1
 
122

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01338
91.6%
1122
 
8.4%
2021-05-01T17:47:18.727107image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:18.780934image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01338
91.6%
1122
 
8.4%

Most occurring characters

ValueCountFrequency (%)
01338
91.6%
1122
 
8.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01338
91.6%
1122
 
8.4%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01338
91.6%
1122
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01338
91.6%
1122
 
8.4%

SaleType_Oth
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1457 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01457
99.8%
13
 
0.2%
2021-05-01T17:47:18.919185image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:18.971038image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01457
99.8%
13
 
0.2%

Most occurring characters

ValueCountFrequency (%)
01457
99.8%
13
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01457
99.8%
13
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01457
99.8%
13
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01457
99.8%
13
 
0.2%

SaleType_WD
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
1
1267 
0
193 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
11267
86.8%
0193
 
13.2%
2021-05-01T17:47:19.102994image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:19.155438image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
11267
86.8%
0193
 
13.2%

Most occurring characters

ValueCountFrequency (%)
11267
86.8%
0193
 
13.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
11267
86.8%
0193
 
13.2%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
11267
86.8%
0193
 
13.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
11267
86.8%
0193
 
13.2%

SaleCondition_AdjLand
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1456 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01456
99.7%
14
 
0.3%
2021-05-01T17:47:19.298170image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:19.350649image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01456
99.7%
14
 
0.3%

Most occurring characters

ValueCountFrequency (%)
01456
99.7%
14
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01456
99.7%
14
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01456
99.7%
14
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01456
99.7%
14
 
0.3%

SaleCondition_Alloca
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1448 
1
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01448
99.2%
112
 
0.8%
2021-05-01T17:47:19.490644image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:19.542643image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01448
99.2%
112
 
0.8%

Most occurring characters

ValueCountFrequency (%)
01448
99.2%
112
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01448
99.2%
112
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01448
99.2%
112
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01448
99.2%
112
 
0.8%

SaleCondition_Family
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1440 
1
 
20

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01440
98.6%
120
 
1.4%
2021-05-01T17:47:19.681835image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:19.733834image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01440
98.6%
120
 
1.4%

Most occurring characters

ValueCountFrequency (%)
01440
98.6%
120
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01440
98.6%
120
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01440
98.6%
120
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01440
98.6%
120
 
1.4%

SaleCondition_Normal
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
1
1198 
0
262 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1
ValueCountFrequency (%)
11198
82.1%
0262
 
17.9%
2021-05-01T17:47:19.864124image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:19.915952image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
11198
82.1%
0262
 
17.9%

Most occurring characters

ValueCountFrequency (%)
11198
82.1%
0262
 
17.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
11198
82.1%
0262
 
17.9%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
11198
82.1%
0262
 
17.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
11198
82.1%
0262
 
17.9%

SaleCondition_Partial
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
0
1335 
1
 
125

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01335
91.4%
1125
 
8.6%
2021-05-01T17:47:20.050597image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-01T17:47:20.102589image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
01335
91.4%
1125
 
8.6%

Most occurring characters

ValueCountFrequency (%)
01335
91.4%
1125
 
8.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1460
100.0%

Most frequent character per category

ValueCountFrequency (%)
01335
91.4%
1125
 
8.6%

Most occurring scripts

ValueCountFrequency (%)
Common1460
100.0%

Most frequent character per script

ValueCountFrequency (%)
01335
91.4%
1125
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1460
100.0%

Most frequent character per block

ValueCountFrequency (%)
01335
91.4%
1125
 
8.6%

Interactions

2021-05-01T17:45:37.905865image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:37.998625image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:38.091827image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:38.179171image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:38.266882image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:38.359989image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:38.448820image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:38.538770image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:38.629791image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:38.716911image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:38.807737image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:38.893027image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:38.985602image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:39.070984image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:39.157334image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:39.250616image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:39.339593image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:39.431293image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:39.518930image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:39.610661image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:39.696796image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:39.787833image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:39.875193image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:39.960114image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:40.055703image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:40.145334image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:40.234094image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:40.317023image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:40.404511image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:40.497601image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:40.585097image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:40.673103image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:40.759450image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:40.844950image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:40.929520image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:41.015521image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:41.108272image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:41.189698image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:41.272965image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:41.363985image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:41.452207image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:41.542467image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:41.631856image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:41.723057image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:41.811521image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:42.090841image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:42.175536image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:42.259923image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:42.353719image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:42.440073image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:42.527171image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:42.608256image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:42.688437image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:42.780514image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:42.866861image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:42.953395image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:43.037031image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:43.120748image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:43.203650image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:43.285907image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:43.375424image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:43.454928image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:43.536017image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:43.623432image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:43.707929image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:43.795919image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:43.881981image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:43.970156image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:44.055412image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:44.138310image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:44.221331image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:44.303599image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:44.393496image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:44.475255image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:44.558230image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:44.639159image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:44.715740image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:44.799214image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:44.883420image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:44.967136image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:45.046934image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:45.126155image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:45.207981image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:45.285265image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:45.367953image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:45.446130image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:45.523593image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:45.606858image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:45.687983image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:45.772741image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:45:45.853850image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
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2021-05-01T17:46:21.336224image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:21.416621image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:21.503000image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:21.580901image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:21.660832image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:21.747654image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:21.834310image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:21.919639image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:22.013695image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:22.112977image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:22.203498image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:22.285704image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:22.370345image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:22.456132image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:22.542734image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:22.626407image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:22.707904image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:22.787225image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:22.866632image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:22.952869image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:23.041482image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:23.128575image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:23.210661image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:23.293959image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:23.376811image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:23.457159image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:23.540279image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:23.621977image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:23.702285image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:23.788471image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:23.873193image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:23.956884image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:24.039259image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:24.129043image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:24.215823image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:24.299411image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:24.382927image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:24.468100image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:24.552953image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:24.639694image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:24.722515image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:24.805042image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:24.885190image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:24.972255image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:25.057393image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:25.143004image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:25.227391image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:25.308942image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:25.393514image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:25.473357image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:25.557429image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:25.634877image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:25.716929image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:25.803490image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:25.888014image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:25.973449image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:26.057046image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:26.143500image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:26.227086image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:26.310434image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:26.392999image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:26.478916image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:26.564533image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:26.649160image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:26.731288image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:26.812656image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:26.891283image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:26.977426image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:27.064030image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:27.152429image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:27.238028image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:27.323100image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:27.405669image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:27.484869image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:27.567829image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:27.645769image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:27.724750image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:27.815372image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:27.901050image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:27.984537image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:28.068766image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:28.160576image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:28.246145image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:28.328014image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:28.416176image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:28.502151image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:28.592785image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:28.682983image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:28.771821image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:28.859149image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:28.947540image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:29.042876image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:29.134093image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:29.224972image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:29.314200image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:29.406563image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:29.495353image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:29.583973image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:29.674013image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:29.757889image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:29.844463image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:29.940483image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:30.031806image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:30.122565image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:30.216439image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:30.308166image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:30.397171image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:30.489092image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-01T17:46:30.577675image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Correlations

2021-05-01T17:47:20.490480image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-05-01T17:47:23.187706image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-05-01T17:47:25.868566image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-05-01T17:47:28.551391image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-05-01T17:47:31.101661image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-05-01T17:46:31.374113image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
A simple visualization of nullity by column.
2021-05-01T17:46:37.999419image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

IdMSSubClassLotFrontageLotAreaStreetLotShapeUtilitiesOverallQualOverallCondYearBuiltYearRemodAddExterQualExterCondBsmtQualBsmtCondBsmtExposureBsmtFinType1BsmtFinSF1BsmtFinType2BsmtUnfSFTotalBsmtSFHeatingQCCentralAir1stFlrSFGrLivAreaBsmtFullBathBsmtHalfBathFullBathHalfBathBedroomAbvGrKitchenAbvGrKitchenQualTotRmsAbvGrdFunctionalFireplacesFireplaceQuGarageYrBltGarageFinishGarageCarsGarageAreaGarageQualGarageCondPavedDriveOpenPorchSFMoSoldYrSoldSalePriceUtilities_was_missingMSZoning_was_missingGarageType_was_missingGarageFinish_was_missingGarageCars_was_missingGarageArea_was_missingGarageQual_was_missingGarageCond_was_missingSaleType_was_missingLotShape_was_missingBsmtExposure_was_missingMSZoning_FVMSZoning_RHMSZoning_RLMSZoning_RMLandContour_HLSLandContour_LowLandContour_LvlLotConfig_CulDSacLotConfig_FR2LotConfig_FR3LotConfig_InsideLandSlope_ModLandSlope_SevNeighborhood_BluesteNeighborhood_BrDaleNeighborhood_BrkSideNeighborhood_ClearCrNeighborhood_CollgCrNeighborhood_CrawforNeighborhood_EdwardsNeighborhood_GilbertNeighborhood_IDOTRRNeighborhood_MeadowVNeighborhood_MitchelNeighborhood_NAmesNeighborhood_NPkVillNeighborhood_NWAmesNeighborhood_NoRidgeNeighborhood_NridgHtNeighborhood_OldTownNeighborhood_SWISUNeighborhood_SawyerNeighborhood_SawyerWNeighborhood_SomerstNeighborhood_StoneBrNeighborhood_TimberNeighborhood_VeenkerCondition1_FeedrCondition1_NormCondition1_PosACondition1_PosNCondition1_RRAeCondition1_RRAnCondition1_RRNeCondition1_RRNnCondition2_FeedrCondition2_NormCondition2_PosACondition2_PosNCondition2_RRAeCondition2_RRAnCondition2_RRNnBldgType_2fmConBldgType_DuplexBldgType_TwnhsBldgType_TwnhsEHouseStyle_1.5UnfHouseStyle_1StoryHouseStyle_2.5FinHouseStyle_2.5UnfHouseStyle_2StoryHouseStyle_SFoyerHouseStyle_SLvlRoofStyle_GableRoofStyle_GambrelRoofStyle_HipRoofStyle_MansardRoofStyle_ShedRoofMatl_CompShgRoofMatl_MembranRoofMatl_MetalRoofMatl_RollRoofMatl_Tar&GrvRoofMatl_WdShakeRoofMatl_WdShnglExterior1st_AsphShnExterior1st_BrkCommExterior1st_BrkFaceExterior1st_CBlockExterior1st_CemntBdExterior1st_HdBoardExterior1st_ImStuccExterior1st_MetalSdExterior1st_PlywoodExterior1st_StoneExterior1st_StuccoExterior1st_VinylSdExterior1st_Wd SdngExterior1st_WdShingExterior2nd_AsphShnExterior2nd_Brk CmnExterior2nd_BrkFaceExterior2nd_CBlockExterior2nd_CmentBdExterior2nd_HdBoardExterior2nd_ImStuccExterior2nd_MetalSdExterior2nd_OtherExterior2nd_PlywoodExterior2nd_StoneExterior2nd_StuccoExterior2nd_VinylSdExterior2nd_Wd SdngExterior2nd_Wd ShngMasVnrType_BrkFaceMasVnrType_NoneMasVnrType_StoneFoundation_CBlockFoundation_PConcFoundation_SlabFoundation_StoneFoundation_WoodHeating_GasAHeating_GasWHeating_GravHeating_OthWHeating_WallElectrical_FuseFElectrical_FusePElectrical_MixElectrical_SBrkrGarageType_AttchdGarageType_BasmentGarageType_BuiltInGarageType_CarPortGarageType_DetchdGarageType_NASaleType_CWDSaleType_ConSaleType_ConLDSaleType_ConLISaleType_ConLwSaleType_NewSaleType_OthSaleType_WDSaleCondition_AdjLandSaleCondition_AllocaSaleCondition_FamilySaleCondition_NormalSaleCondition_Partial
016065.084502040407520032003403040301060706101508565018561710102131408800402003.020254830303061220082085000000000000000100010001000000100000000000000000000100000001000000000000010010000100000000000000000100000000000000100100010001000000011000000000000100010
122080.09600204040681976197630304030405097810284126250112621262012031306801301976.02024603030300520071815000000000000000100010100000000000000000000000000011000000001000000000010000010000100000000000001000000000000010000000010100001000000011000000000000100010
236068.0112502030407520012002403040302060486104349205019201786102131406801302001.020260830303042920082235000000000000000100010001000000100000000000000000000100000001000000000000010010000100000000000000000100000000000000100100010001000000011000000000000100010
347060.095502030407519151970303030401050216105407564019611717101031407801401998.010364230303035220061400000000000000000100010000000000010000000000000000000100000001000000000000010010000100000000000000000010000000000000001010000001000000010000100000000100000
456084.014260203040852000200040304030306065510490114550111452198102141409801302000.0203836303030841220082500000000000000000100010100000000000000000010000000000100000001000000000000010010000100000000000000000100000000000000100100010001000000011000000000000100010
565085.014115203040551993199530304030106073210647965017961362101111305800401993.0102480303030301020091430000000000000000100010001000000000000100000000000000100000001000000000000000010000100000000000000000100000000000000100010000011000000011000000000000100010
672075.0100842040408520042005403050303060136910317168650116941694102031407801402004.020263630303057820073070000000000000000100010001000000000000000000000010000100000001000000000010000010000100000000000000000100000000000000100001010001000000011000000000000100010
786060.010382203040761973197330304030205085940216110750111072090102131307802301973.02024843030302041120092000000000000000000100010000000000000000000100000000000001000001000000000000010010000100000000000100000000000001000000000001100001000000011000000000000100010
895051.06120204040751931195030303030101001095295240110221774002022308702301931.01024682030300420081299000000000000000010010001000000000000000000100000000000000001000000000000000010000100000000100000000000000000000000001010000001000010000000100000000100000
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Last rows

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